SemEval-2026 Task 7: Everyday Knowledge Across Diverse Languages and Cultures

Aleksandra Edwards; Alice Oh; Amr Keleg; Asahi Ushio; Bryan Tan; Carla Perez-Almendros; Chenyang Lyu; Christine de Kock; Huda Hakami; James Barry

SemEval-2026 Task 7: Everyday Knowledge Across Diverse Languages and Cultures

T0 review · 0 major / 1 minor · reviewed 2026-05-09 · grok-4.3

Challenge this review Download PDF Read on arXiv ↗

Pith's one-line read A benchmark evaluates language models on everyday knowledge across more than 30 languages and cultures without permitting training on the test data.

desk verdict This is a standard shared-task overview that scales an existing cultural knowledge benchmark to 30+ low-resource languages and draws decent participation, but it introduces no new methods or independent validation. read the letter →

arxiv 2605.02601 v1 submitted 2026-05-04 cs.CL

Nedjma Ousidhoum , Junho Myung , Carla Perez-Almendros , Jiho Jin , Amr Keleg , Meriem Beloucif , Yi Zhou , Rodrigo Agerri

show 22 more authors

Vladimir Araujo Naomi Baes James Barry Joanne Boisson Nancy F. Chen Christine de Kock Aleksandra Edwards Joseba Fernandez de Landa Mohamed Fazli Imam Huda Hakami Shu-Kai Hsieh Joseph Marvin Imperial Roy Ka-Wei Lee Zhengyuan Liu Chenyang Lyu Younes Samih Johan Sjons Bryan Tan Asahi Ushio Weihua Zheng Alice Oh Jose Camacho-Collados

This is my paper · ORCID

classification cs.CL

keywords sharedtaskmultilingualNLPculturalknowledgelow-resourcelanguagesLLMevaluationeverydaybenchmarkquestionanswering

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper presents a shared task designed to measure how well natural language processing systems can handle everyday knowledge questions in a wide range of languages and cultures. It relies on an extended set of manually created questions covering more than 30 language-culture pairs, with emphasis on low-resource languages from different continents. The task features two question formats, short-answer and multiple-choice, and enforces strict evaluation-only use of the data. Submissions from 62 teams were analyzed to identify effective strategies and persistent difficulties in model behavior for under-represented groups.

What carries the argument

The extended benchmark of everyday knowledge questions in short-answer and multiple-choice formats applied to more than 30 language-culture pairs.

What would settle it

A finding that the benchmark questions systematically miss or misrepresent knowledge held by speakers of the included languages would undermine the task's validity as a measure of cultural adaptability.

Watch

Extended reading notes

Core claim

The paper establishes that by organizing this evaluation-focused task on an extended benchmark of everyday knowledge, it is possible to gather comparable results from many systems and uncover shared insights about challenges in handling linguistic and cultural diversity, particularly for low-resource settings.

Load-bearing premise

The questions in the benchmark accurately represent typical everyday knowledge in each of the covered cultures without introducing bias.

Editorial extensions

If this is right

The no-training rule ensures that results reflect genuine generalization rather than memorization.
Analysis of top systems reveals common approaches to multilingual question answering.
The task highlights open questions around model misalignment with cultural contexts.
Performance on low-resource languages indicates areas needing further development in NLP systems.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Such benchmarks could inform the creation of more inclusive AI models that respect cultural differences.
Extending this approach to additional knowledge domains or languages might expose further limitations in current technology.
The observed challenges suggest that evaluation methods themselves may need refinement to better capture cultural nuances.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Desk Editor's Note

read the letter

This paper sets up SemEval-2026 Task 7 as an evaluation-only shared task on everyday knowledge in LLMs across more than 30 language-culture pairs, mostly low-resource. It extends the prior BLEnD benchmark, splits the data into short-answer and multiple-choice tracks, and enforces a strict no-training rule so submissions stay in evaluation mode only. They got 140 registrations and 62 final teams, plus 19 system papers, which is respectable turnout for this kind of effort. The write-up includes some summary of what the stronger submissions did and flags a few recurring issues around misalignment in low-resource settings. That documentation is the practical part people can actually use. The main limitation is that the entire exercise rests on the manual questions from the 2024 BLEnD paper. This version adds no new checks for cultural accuracy, annotator bias, or whether the items really capture everyday knowledge rather than the creators' view of it. Without that, the participant results are hard to interpret beyond “these models still struggle here.” The analysis of approaches is fine as far as it goes, but it is secondary to the benchmark quality. This is mainly for groups that run or consume multilingual evaluation benchmarks. A reader working on low-resource NLP or cultural fairness might pick up concrete ideas from the system descriptions and the open questions they list. It is not a methods paper and does not claim new theoretical ground, so most people outside that niche will not need it. I would bring it to a reading group only if the group is actively comparing evaluation resources. I would not cite it in my own work unless I were referencing the task data or participation stats. It deserves peer review because these task papers document where the field is measuring progress on a concrete gap, and the scale here makes the record worth having.

Referee Report

0 major / 1 minor

Summary. The manuscript presents SemEval-2026 Task 7, a shared task for evaluating the adaptability of LLMs and NLP systems to everyday knowledge across diverse languages and cultures. The task data are an extended version of the manually constructed BLEnD benchmark covering more than 30 language-culture pairs (predominantly low-resource languages). It defines two tracks—Short-Answer Questions (SAQ) and Multiple-Choice Questions (MCQ)—with strict rules prohibiting any use of the data for training, fine-tuning, or few-shot adaptation. The paper reports 140 registered participants, 62 final submissions, 19 system description papers, and provides analysis of the best-performing systems, common approaches, and open challenges in evaluation, misalignment, and model behavior for low-resource languages and under-represented cultures.

Significance. If the reported results and analysis hold, the work supplies a large-scale, culturally diverse evaluation framework that can serve as a reference benchmark for assessing cultural knowledge and alignment in LLMs, especially in low-resource settings. The high participation rate and the evaluation-only constraint strengthen the reliability of any comparative findings, while the analysis of adopted modeling strategies offers practical insights for future work on cross-cultural NLP.

minor comments (1)

[Abstract] Abstract: the statement that results and analysis are reported would be strengthened by an explicit forward reference to the relevant section or table containing the quantitative performance metrics and error analysis of the top systems.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive review, accurate summary of the task, and recommendation to accept. The feedback correctly identifies the value of the evaluation-only constraint and the insights from high participation rates.

Circularity Check

0 steps flagged · score 1.0 of 10

Descriptive shared-task paper with no derivations or load-bearing circularity

full rationale

The manuscript is a standard SemEval task description whose central statements are factual descriptions of data provenance and participation statistics. It contains no equations, fitted parameters, predictions, or modeling derivations. The single self-citation to Myung et al. 2024 simply identifies the source benchmark being extended; this reference is not used to justify any internal claim that would otherwise be unsupported, nor does any result reduce to the citation by construction. All other content (track definitions, submission rules, result reporting) is observational and does not rely on unverified self-referential logic.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

The paper introduces no free parameters, mathematical axioms, or invented entities; it relies on standard assumptions in NLP evaluation such as the validity of benchmark-based testing and participant compliance with rules.

assumptions (1)

domain assumption NLP systems can be meaningfully evaluated on knowledge benchmarks without training or fine-tuning on the test data itself.
The task explicitly prohibits any form of model modification using the benchmark data.

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Cite this review

Pith. "Pith review of SemEval-2026 Task 7: Everyday Knowledge Across Diverse Languages and Cultures." pith.science (2026). https://pith.science/paper/2605.02601

@misc{pith2026260502601,
  author       = {Pith},
  title        = {Pith review of: SemEval-2026 Task 7: Everyday Knowledge Across Diverse Languages and Cultures},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2605.02601}},
  note         = {Machine review of arXiv:2605.02601}
}

read the original abstract

We present our shared task on evaluating the adaptability of LLMs and NLP systems across multiple languages and cultures. The task data consist of an extended version of our manually constructed BLEnD benchmark (Myung et al. 2024), covering more than 30 language-culture pairs, predominantly representing low-resource languages spoken across multiple continents. As the task is designed strictly for evaluation, participants were not permitted to use the data for training, fine-tuning, few-shot learning, or any other form of model modification. Our task includes two tracks: (a) Short-Answer Questions (SAQ) and (b) Multiple-Choice Questions (MCQ). Participants were required to predict labels and were allowed to submit any NLP system and adopt diverse modelling strategies, provided that the benchmark was used solely for evaluation. The task attracted more than 140 registered participants, and we received final submissions from 62 teams, along with 19 system description papers. We report the results and present an analysis of the best-performing systems and the most commonly adopted approaches. Furthermore, we discuss shared insights into open questions and challenges related to evaluation, misalignment, and methodological perspectives on model behaviour in low-resource languages and for under-represented cultures.

Figures

Figures reproduced from arXiv: 2605.02601 by the authors.

**Figure 1.** Data creation pipeline: recruitment of local speakers for annotation, native-speaker quality control without the use of LLMs or search engines, and extension to 17 additional languages. 2024; Pawar et al., 2024; Liu et al., 2025). However, such models often exhibit substantial limitations in culture-specific knowledge, particularly when handling under-resourced languages or nonWestern regions. They tend to gener… view at source ↗

**Figure 2.** Language–culture pairs represented in our BLEnD benchmark. Africa: Arabic (Algeria, Egypt, Morocco), Amharic (Ethiopia), Hausa (Northern Nigeria). Asia: Assamese (Assam, India), Azerbaijani (Azerbaijan), Mandarin (China), Indonesian (Indonesia), Javanese (West Java, Indonesia), Persian (Iran), Korean (North and South Korea), Arabic (Saudi Arabia), Japanese (Japan), Tagalog (Philippines), Tamil (Sri Lanka, Singapor… view at source ↗

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2024 doi
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H ausa H ate: An Expert Annotated Corpus for H ausa Hate Speech Detection

Vargas, Francielle and Guimar \ a es, Samuel and Muhammad, Shamsuddeen Hassan and Alves, Diego and Ahmad, Ibrahim Said and Abdulmumin, Idris and Mohamed, Diallo and Pardo, Thiago and Benevenuto, Fabr \'i cio. H ausa H ate: An Expert Annotated Corpus for H ausa Hate Speech Dete...

2024 doi
[100]

VIDA : The Visual Incel Data Archive

Anastasi, Selenia and Schneider, Florian and Biemann, Chris and Fischer, Tim. VIDA : The Visual Incel Data Archive. A Theory-oriented Annotated Dataset To Enhance Hate Detection Through Visual Culture. 2024. doi:10.18653/v1/2024.woah-1.6

2024 doi
[101]

Towards a Unified Framework for Adaptable Problematic Content Detection via Continual Learning

Omrani, Ali and Salkhordeh Ziabari, Alireza and Golazizian, Preni and Sorensen, Jeffrey and Dehghani, Morteza. Towards a Unified Framework for Adaptable Problematic Content Detection via Continual Learning. 2024. doi:10.18653/v1/2024.woah-1.7

2024 doi
[102]

From Linguistics to Practice: a Case Study of Offensive Language Taxonomy in H ebrew

Liebeskind, Chaya and Litvak, Marina and Vanetik, Natalia. From Linguistics to Practice: a Case Study of Offensive Language Taxonomy in H ebrew. 2024. doi:10.18653/v1/2024.woah-1.8

2024 doi
[103]

Estimating the Emotion of Disgust in G reek Parliament Records

Lislevand, Vanessa and Pavlopoulos, John and Louridas, Panos and Dritsa, Konstantina. Estimating the Emotion of Disgust in G reek Parliament Records. 2024. doi:10.18653/v1/2024.woah-1.9

2024 doi
[104]

Simple LLM based Approach to Counter Algospeak

Fillies, Jan and Paschke, Adrian. Simple LLM based Approach to Counter Algospeak. 2024. doi:10.18653/v1/2024.woah-1.10

2024 doi
[105]

Harnessing Personalization Methods to Identify and Predict Unreliable Information Spreader Behavior

Ashraf, Shaina and Gruschka, Fabio and Flek, Lucie and Welch, Charles. Harnessing Personalization Methods to Identify and Predict Unreliable Information Spreader Behavior. 2024. doi:10.18653/v1/2024.woah-1.11

2024 doi
[106]

Robust Safety Classifier Against Jailbreaking Attacks: Adversarial Prompt Shield

Kim, Jinhwa and Derakhshan, Ali and Harris, Ian. Robust Safety Classifier Against Jailbreaking Attacks: Adversarial Prompt Shield. 2024. doi:10.18653/v1/2024.woah-1.12

2024 doi
[107]

Improving aggressiveness detection using a data augmentation technique based on a Diffusion Language Model

Reyes-Ram \'i rez, Antonio and Arag \'o n, Mario and S \'a nchez-Vega, Fernando and L \'o pez-Monroy, Adrian. Improving aggressiveness detection using a data augmentation technique based on a Diffusion Language Model. 2024. doi:10.18653/v1/2024.woah-1.13

2024 doi
[108]

The M exican Gayze: A Computational Analysis of the Attitudes towards the LGBT + Population in M exico on Social Media Across a Decade

Andersen, Scott and Ojeda-Trueba, Segio-Luis and V \'a squez, Juan and Bel-Enguix, Gemma. The M exican Gayze: A Computational Analysis of the Attitudes towards the LGBT + Population in M exico on Social Media Across a Decade. 2024. doi:10.18653/v1/2024.woah-1.14

2024 doi
[109]

X -posing Free Speech: Examining the Impact of Moderation Relaxation on Online Social Networks

Arun, Arvindh and Chhatani, Saurav and An, Jisun and Kumaraguru, Ponnurangam. X -posing Free Speech: Examining the Impact of Moderation Relaxation on Online Social Networks. 2024. doi:10.18653/v1/2024.woah-1.15

2024 doi
[110]

The Uli Dataset: An Exercise in Experience Led Annotation of o GBV

Arora, Arnav and Jinadoss, Maha and Arora, Cheshta and George, Denny and Brindaalakshmi and Khan, Haseena and Rawat, Kirti and Div and Ritash and Mathur, Seema. The Uli Dataset: An Exercise in Experience Led Annotation of o GBV. 2024. doi:10.18653/v1/2024.woah-1.16

2024 doi
[111]

Towards Interpretable Hate Speech Detection using Large Language Model-extracted Rationales

Nirmal, Ayushi and Bhattacharjee, Amrita and Sheth, Paras and Liu, Huan. Towards Interpretable Hate Speech Detection using Large Language Model-extracted Rationales. 2024. doi:10.18653/v1/2024.woah-1.17

2024 doi
[112]

A B ayesian Quantification of Aporophobia and the Aggravating Effect of Low -- Wealth Contexts on Stigmatization

Brate, Ryan and Van Erp, Marieke and Van Den Bosch, Antal. A B ayesian Quantification of Aporophobia and the Aggravating Effect of Low -- Wealth Contexts on Stigmatization. 2024. doi:10.18653/v1/2024.woah-1.18

2024 doi
[113]

Toxicity Classification in U krainian

Dementieva, Daryna and Khylenko, Valeriia and Babakov, Nikolay and Groh, Georg. Toxicity Classification in U krainian. 2024. doi:10.18653/v1/2024.woah-1.19

2024 doi
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A Strategy Labelled Dataset of Counterspeech

Poudhar, Aashima and Konstas, Ioannis and Abercrombie, Gavin. A Strategy Labelled Dataset of Counterspeech. 2024. doi:10.18653/v1/2024.woah-1.20

2024 doi
[115]

Improving Covert Toxicity Detection by Retrieving and Generating References

Lee, Dong-Ho and Cho, Hyundong and Jin, Woojeong and Moon, Jihyung and Park, Sungjoon and R. Improving Covert Toxicity Detection by Retrieving and Generating References. 2024. doi:10.18653/v1/2024.woah-1.21

2024 doi
[116]

Subjective Isms? On the Danger of Conflating Hate and Offence in Abusive Language Detection

Cercas Curry, Amanda and Abercrombie, Gavin and Talat, Zeerak. Subjective Isms? On the Danger of Conflating Hate and Offence in Abusive Language Detection. 2024. doi:10.18653/v1/2024.woah-1.22

2024 doi
[117]

From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasets

Tonneau, Manuel and Liu, Diyi and Fraiberger, Samuel and Schroeder, Ralph and Hale, Scott and R. From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasets. 2024. doi:10.18653/v1/2024.woah-1.23

2024 doi
[118]

SGH ate C heck: Functional Tests for Detecting Hate Speech in Low-Resource Languages of S ingapore

Ng, Ri Chi and Prakash, Nirmalendu and Hee, Ming Shan and Choo, Kenny Tsu Wei and Lee, Roy Ka-wei. SGH ate C heck: Functional Tests for Detecting Hate Speech in Low-Resource Languages of S ingapore. 2024. doi:10.18653/v1/2024.woah-1.24

2024 doi
[119]

Proceedings of the Ninth Workshop on Noisy and User-generated Text (W-NUT 2024). 2024

2024
[120]

Correcting Challenging F innish Learner Texts With Claude, GPT -3.5 and GPT -4 Large Language Models

Creutz, Mathias. Correcting Challenging F innish Learner Texts With Claude, GPT -3.5 and GPT -4 Large Language Models. 2024

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[121]

Context-aware Adversarial Attack on Named Entity Recognition

Chen, Shuguang and Neves, Leonardo and Solorio, Thamar. Context-aware Adversarial Attack on Named Entity Recognition. 2024

2024
[122]

Effects of different types of noise in user-generated reviews on human and machine translations including C hat GPT

Popovic, Maja and Lapshinova-Koltunski, Ekaterina and Koponen, Maarit. Effects of different types of noise in user-generated reviews on human and machine translations including C hat GPT. 2024

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Stanceosaurus 2.0 - Classifying Stance Towards R ussian and S panish Misinformation

Lavrouk, Anton and Ligon, Ian and Zheng, Jonathan and Naous, Tarek and Xu, Wei and Ritter, Alan. Stanceosaurus 2.0 - Classifying Stance Towards R ussian and S panish Misinformation. 2024

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Elahi, Kazi and Rahman, Tasnuva and Shahriar, Shakil and Sarker, Samir and Shawon, Md. and Shahariar, G. M. A Comparative Analysis of Noise Reduction Methods in Sentiment Analysis on Noisy B angla Texts. 2024

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Label Supervised Contrastive Learning for Imbalanced Text Classification in E uclidean and Hyperbolic Embedding Spaces

Khalid, Baber and Dai, Shuyang and Taghavi, Tara and Lee, Sungjin. Label Supervised Contrastive Learning for Imbalanced Text Classification in E uclidean and Hyperbolic Embedding Spaces. 2024

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M aint N orm: A corpus and benchmark model for lexical normalisation and masking of industrial maintenance short text

Bikaun, Tyler and Hodkiewicz, Melinda and Liu, Wei. M aint N orm: A corpus and benchmark model for lexical normalisation and masking of industrial maintenance short text. 2024

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[127]

The Effects of Data Quality on Named Entity Recognition

Bhadauria, Divya and Sierra M \'u nera, Alejandro and Krestel, Ralf. The Effects of Data Quality on Named Entity Recognition. 2024

2024
[128]

Topic Bias in Emotion Classification

Wegge, Maximilian and Klinger, Roman. Topic Bias in Emotion Classification. 2024

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[129]

Stars Are All You Need: A Distantly Supervised Pyramid Network for Unified Sentiment Analysis

Li, Wenchang and Chen, Yixing and Zheng, Shuang and Wang, Lei and Lalor, John. Stars Are All You Need: A Distantly Supervised Pyramid Network for Unified Sentiment Analysis. 2024

2024
[130]

Proceedings of the The 6th Workshop on Narrative Understanding. 2024. doi:10.18653/v1/2024.wnu-1.0

2024 doi
[131]

Narration as Functions: from Events to Narratives

Huang, Junbo and Usbeck, Ricardo. Narration as Functions: from Events to Narratives. 2024. doi:10.18653/v1/2024.wnu-1.1

2024 doi
[132]

How to tame your plotline: A framework for goal-driven interactive fairy tale generation

Ermolaeva, Marina and Shakhmatova, Anastasia and Nepomnyashchikh, Alina and Fenogenova, Alena. How to tame your plotline: A framework for goal-driven interactive fairy tale generation. 2024. doi:10.18653/v1/2024.wnu-1.2

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[133]

Understanding Transmedia Storytelling: Reception and Narrative Comprehension in Bill Willingham`s Fables Franchise

Lagrange, Victoria. Understanding Transmedia Storytelling: Reception and Narrative Comprehension in Bill Willingham`s Fables Franchise. 2024. doi:10.18653/v1/2024.wnu-1.3

2024 doi
[134]

Using Large Language Models for Understanding Narrative Discourse

Piper, Andrew and Bagga, Sunyam. Using Large Language Models for Understanding Narrative Discourse. 2024. doi:10.18653/v1/2024.wnu-1.4

2024 doi
[135]

Is It Safe to Tell Your Story? Towards Achieving Privacy for Sensitive Narratives

Shokri, Mohammad and Bishop, Allison and Levitan, Sarah Ita. Is It Safe to Tell Your Story? Towards Achieving Privacy for Sensitive Narratives. 2024. doi:10.18653/v1/2024.wnu-1.7

2024 doi
[136]

Annotating Mystery Novels: Guidelines and Adaptations

Heyns, Nuette and Van Zaanen, Menno. Annotating Mystery Novels: Guidelines and Adaptations. 2024. doi:10.18653/v1/2024.wnu-1.9

2024 doi
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Causal Micro-Narratives

Heddaya, Mourad and Zeng, Qingcheng and Zentefis, Alexander and Voigt, Rob and Tan, Chenhao. Causal Micro-Narratives. 2024. doi:10.18653/v1/2024.wnu-1.12

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Media Framing through the Lens of Event-Centric Narratives

Das, Rohan and Chandra, Aditya and Lee, I-Ta and Pacheco, Maria Leonor. Media Framing through the Lens of Event-Centric Narratives. 2024. doi:10.18653/v1/2024.wnu-1.15

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[139]

BERT -based Annotation of Oral Texts Elicited via Multilingual Assessment Instrument for Narratives

Baumann, Timo and Eller, Korbinian and Gagarina, Natalia. BERT -based Annotation of Oral Texts Elicited via Multilingual Assessment Instrument for Narratives. 2024. doi:10.18653/v1/2024.wnu-1.16

2024 doi
[140]

Proceedings of the Ninth Conference on Machine Translation. 2024. doi:10.18653/v1/2024.wmt-1.0

2024 doi
[141]

Findings of the WMT 24 General Machine Translation Shared Task: The LLM Era Is Here but MT Is Not Solved Yet

Kocmi, Tom and Avramidis, Eleftherios and Bawden, Rachel and Bojar, Ond r ej and Dvorkovich, Anton and Federmann, Christian and Fishel, Mark and Freitag, Markus and Gowda, Thamme and Grundkiewicz, Roman and Haddow, Barry and Karpinska, Marzena and Koehn, Philipp and Marie, Ben...

2024 doi
[142]

Are LLM s Breaking MT Metrics? Results of the WMT 24 Metrics Shared Task

Freitag, Markus and Mathur, Nitika and Deutsch, Daniel and Lo, Chi-Kiu and Avramidis, Eleftherios and Rei, Ricardo and Thompson, Brian and Blain, Frederic and Kocmi, Tom and Wang, Jiayi and Adelani, David Ifeoluwa and Buchicchio, Marianna and Zerva, Chrysoula and Lavie, Alon. ...

2024 doi
[143]

De Souza, Jos \'e G

Zerva, Chrysoula and Blain, Frederic and C. De Souza, Jos \'e G. and Kanojia, Diptesh and Deoghare, Sourabh and Guerreiro, Nuno M. and Attanasio, Giuseppe and Rei, Ricardo and Orasan, Constantin and Negri, Matteo and Turchi, Marco and Chatterjee, Rajen and Bhattacharyya, Pushp...

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[144]

Findings of the WMT 2024 Shared Task of the Open Language Data Initiative

Maillard, Jean and Burchell, Laurie and Anastasopoulos, Antonios and Federmann, Christian and Koehn, Philipp and Wang, Skyler. Findings of the WMT 2024 Shared Task of the Open Language Data Initiative. 2024. doi:10.18653/v1/2024.wmt-1.4

2024 doi
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Results of the WAT / WMT 2024 Shared Task on Patent Translation

Higashiyama, Shohei. Results of the WAT / WMT 2024 Shared Task on Patent Translation. 2024. doi:10.18653/v1/2024.wmt-1.5

2024 doi
[146]

Findings of the WMT 2024 Biomedical Translation Shared Task: Test Sets on Abstract Level

Neves, Mariana and Grozea, Cristian and Thomas, Philippe and Roller, Roland and Bawden, Rachel and N \'e v \'e ol, Aur \'e lie and Castle, Steffen and Bonato, Vanessa and Di Nunzio, Giorgio Maria and Vezzani, Federica and Vicente Navarro, Maika and Yeganova, Lana and Jimeno Ye...

2024 doi
[147]

MSLC 24 Submissions to the General Machine Translation Task

Larkin, Samuel and Lo, Chi-Kiu and Knowles, Rebecca. MSLC 24 Submissions to the General Machine Translation Task. 2024. doi:10.18653/v1/2024.wmt-1.7

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IOL Research Machine Translation Systems for WMT 24 General Machine Translation Shared Task

Zhang, Wenbo. IOL Research Machine Translation Systems for WMT 24 General Machine Translation Shared Task. 2024. doi:10.18653/v1/2024.wmt-1.8

2024 doi
[149]

Choose the Final Translation from NMT and LLM Hypotheses Using MBR Decoding: HW - TSC `s Submission to the WMT 24 General MT Shared Task

Wu, Zhanglin and Wei, Daimeng and Li, Zongyao and Shang, Hengchao and Guo, Jiaxin and Li, Shaojun and Rao, Zhiqiang and Luo, Yuanchang and Xie, Ning and Yang, Hao. Choose the Final Translation from NMT and LLM Hypotheses Using MBR Decoding: HW - TSC `s Submission to the WMT 24...

2024 doi
[150]

C ycle GN : A Cycle Consistent Approach for Neural Machine Translation

Dreano, S. C ycle GN : A Cycle Consistent Approach for Neural Machine Translation. 2024. doi:10.18653/v1/2024.wmt-1.10

2024 doi
[151]

U v A - MT `s Participation in the WMT 24 General Translation Shared Task

Tan, Shaomu and Stap, David and Aycock, Seth and Monz, Christof and Wu, Di. U v A - MT `s Participation in the WMT 24 General Translation Shared Task. 2024. doi:10.18653/v1/2024.wmt-1.11

2024 doi
[152]

Rei, Ricardo and Pombal, Jose and Guerreiro, Nuno M. and Alves, Jo \ a o and Martins, Pedro Henrique and Fernandes, Patrick and Wu, Helena and Vaz, Tania and Alves, Duarte and Farajian, Amin and Agrawal, Sweta and Farinhas, Antonio and C. De Souza, Jos \'e G. and Martins, Andr...

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TSU HITS `s Submissions to the WMT 2024 General Machine Translation Shared Task

Mynka, Vladimir and Mikhaylovskiy, Nikolay. TSU HITS `s Submissions to the WMT 2024 General Machine Translation Shared Task. 2024. doi:10.18653/v1/2024.wmt-1.13

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[154]

Document-level Translation with LLM Reranking: Team- J at WMT 2024 General Translation Task

Kudo, Keito and Deguchi, Hiroyuki and Morishita, Makoto and Fujii, Ryo and Ito, Takumi and Ozaki, Shintaro and Natsumi, Koki and Sato, Kai and Yano, Kazuki and Takahashi, Ryosuke and Kimura, Subaru and Hara, Tomomasa and Sakai, Yusuke and Suzuki, Jun. Document-level Translatio...

2024 doi
[155]

DLUT and GTCOM `s Neural Machine Translation Systems for WMT 24

Zong, Hao and Bei, Chao and Liu, Huan and Yuan, Conghu and Chen, Wentao and Huang, Degen. DLUT and GTCOM `s Neural Machine Translation Systems for WMT 24. 2024. doi:10.18653/v1/2024.wmt-1.15

2024 doi
[156]

CUNI at WMT 24 General Translation Task: LLM s, ( Q ) L o RA , CPO and Model Merging

Hrabal, Miroslav and Jon, Josef and Popel, Martin and Luu, Nam and Semin, Danil and Bojar, Ond r ej. CUNI at WMT 24 General Translation Task: LLM s, ( Q ) L o RA , CPO and Model Merging. 2024. doi:10.18653/v1/2024.wmt-1.16

2024 doi
[157]

From General LLM to Translation: How We Dramatically Improve Translation Quality Using Human Evaluation Data for LLM Finetuning

Elshin, Denis and Karpachev, Nikolay and Gruzdev, Boris and Golovanov, Ilya and Ivanov, Georgy and Antonov, Alexander and Skachkov, Nickolay and Latypova, Ekaterina and Layner, Vladimir and Enikeeva, Ekaterina and Popov, Dmitry and Chekashev, Anton and Negodin, Vladislav and F...

2024 doi
[158]

Cogs in a Machine, Doing What They`re Meant to Do -- the AMI Submission to the WMT 24 General Translation Task

Jasonarson, Atli and Hafsteinsson, Hinrik and \'A rmannsson, Bjarki and Steingr \'i msson, Steinth \'o r. Cogs in a Machine, Doing What They`re Meant to Do -- the AMI Submission to the WMT 24 General Translation Task. 2024. doi:10.18653/v1/2024.wmt-1.18

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IKUN for WMT 24 General MT Task: LLM s Are Here for Multilingual Machine Translation

Liao, Baohao and Herold, Christian and Khadivi, Shahram and Monz, Christof. IKUN for WMT 24 General MT Task: LLM s Are Here for Multilingual Machine Translation. 2024. doi:10.18653/v1/2024.wmt-1.19

2024 doi
[160]

NTTSU at WMT 2024 General Translation Task

Kondo, Minato and Fukuda, Ryo and Wang, Xiaotian and Chousa, Katsuki and Nishimura, Masato and Buma, Kosei and Kano, Takatomo and Utsuro, Takehito. NTTSU at WMT 2024 General Translation Task. 2024. doi:10.18653/v1/2024.wmt-1.20

2024 doi
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SCIR - MT `s Submission for WMT 24 General Machine Translation Task

Li, Baohang and Ye, Zekai and Huang, Yichong and Feng, Xiaocheng and Qin, Bing. SCIR - MT `s Submission for WMT 24 General Machine Translation Task. 2024. doi:10.18653/v1/2024.wmt-1.21

2024 doi
[162]

AIST AIRC Systems for the WMT 2024 Shared Tasks

Rikters, Matiss and Miwa, Makoto. AIST AIRC Systems for the WMT 2024 Shared Tasks. 2024. doi:10.18653/v1/2024.wmt-1.22

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Occiglot at WMT 24: E uropean Open-source Large Language Models Evaluated on Translation

Avramidis, Eleftherios and Gr. Occiglot at WMT 24: E uropean Open-source Large Language Models Evaluated on Translation. 2024. doi:10.18653/v1/2024.wmt-1.23

2024 doi
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C o ST of breaking the LLM s

Mukherjee, Ananya and Yadav, Saumitra and Shrivastava, Manish. C o ST of breaking the LLM s. 2024. doi:10.18653/v1/2024.wmt-1.24

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WMT 24 Test Suite: Gender Resolution in Speaker-Listener Dialogue Roles

Dawkins, Hillary and Nejadgholi, Isar and Lo, Chi-Kiu. WMT 24 Test Suite: Gender Resolution in Speaker-Listener Dialogue Roles. 2024. doi:10.18653/v1/2024.wmt-1.25

2024 doi
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The G ender Q ueer Test Suite

Friidhriksd \'o ttir, Steinunn Rut. The G ender Q ueer Test Suite. 2024. doi:10.18653/v1/2024.wmt-1.26

2024 doi
[167]

Domain Dynamics: Evaluating Large Language Models in E nglish- H indi Translation

Bhattacharjee, Soham and Gain, Baban and Ekbal, Asif. Domain Dynamics: Evaluating Large Language Models in E nglish- H indi Translation. 2024. doi:10.18653/v1/2024.wmt-1.27

2024 doi
[168]

Investigating the Linguistic Performance of Large Language Models in Machine Translation

Manakhimova, Shushen and Macketanz, Vivien and Avramidis, Eleftherios and Lapshinova-Koltunski, Ekaterina and Bagdasarov, Sergei and M. Investigating the Linguistic Performance of Large Language Models in Machine Translation. 2024. doi:10.18653/v1/2024.wmt-1.28

2024 doi
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I so C hrono M eter: A Simple and Effective Isochronic Translation Evaluation Metric

Rozanov, Nikolai and Pankov, Vikentiy and Mukhutdinov, Dmitrii and Vypirailenko, Dima. I so C hrono M eter: A Simple and Effective Isochronic Translation Evaluation Metric. 2024. doi:10.18653/v1/2024.wmt-1.29

2024 doi
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A Test Suite of Prompt Injection Attacks for LLM -based Machine Translation

Miceli Barone, Antonio Valerio and Sun, Zhifan. A Test Suite of Prompt Injection Attacks for LLM -based Machine Translation. 2024. doi:10.18653/v1/2024.wmt-1.30

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Killing Two Flies with One Stone: An Attempt to Break LLM s Using E nglish- I celandic Idioms and Proper Names

\'A rmannsson, Bjarki and Hafsteinsson, Hinrik and Jasonarson, Atli and Steingrimsson, Steinthor. Killing Two Flies with One Stone: An Attempt to Break LLM s Using E nglish- I celandic Idioms and Proper Names. 2024. doi:10.18653/v1/2024.wmt-1.31

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M eta M etrics- MT : Tuning Meta-Metrics for Machine Translation via Human Preference Calibration

Anugraha, David and Kuwanto, Garry and Susanto, Lucky and Wijaya, Derry Tanti and Winata, Genta. M eta M etrics- MT : Tuning Meta-Metrics for Machine Translation via Human Preference Calibration. 2024. doi:10.18653/v1/2024.wmt-1.32

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chr F - S : Semantics Is All You Need

Mukherjee, Ananya and Shrivastava, Manish. chr F - S : Semantics Is All You Need. 2024. doi:10.18653/v1/2024.wmt-1.33

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MSLC 24: Further Challenges for Metrics on a Wide Landscape of Translation Quality

Knowles, Rebecca and Larkin, Samuel and Lo, Chi-Kiu. MSLC 24: Further Challenges for Metrics on a Wide Landscape of Translation Quality. 2024. doi:10.18653/v1/2024.wmt-1.34

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M etric X -24: The G oogle Submission to the WMT 2024 Metrics Shared Task

Juraska, Juraj and Deutsch, Daniel and Finkelstein, Mara and Freitag, Markus. M etric X -24: The G oogle Submission to the WMT 2024 Metrics Shared Task. 2024. doi:10.18653/v1/2024.wmt-1.35

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Evaluating WMT 2024 Metrics Shared Task Submissions on A fri MTE (the A frican Challenge Set)

Wang, Jiayi and Adelani, David Ifeoluwa and Stenetorp, Pontus. Evaluating WMT 2024 Metrics Shared Task Submissions on A fri MTE (the A frican Challenge Set). 2024. doi:10.18653/v1/2024.wmt-1.36

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Machine Translation Metrics Are Better in Evaluating Linguistic Errors on LLM s than on Encoder-Decoder Systems

Avramidis, Eleftherios and Manakhimova, Shushen and Macketanz, Vivien and M. Machine Translation Metrics Are Better in Evaluating Linguistic Errors on LLM s than on Encoder-Decoder Systems. 2024. doi:10.18653/v1/2024.wmt-1.37

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TMU - HIT `s Submission for the WMT 24 Quality Estimation Shared Task: Is GPT -4 a Good Evaluator for Machine Translation?

Sato, Ayako and Nakajima, Kyotaro and Kim, Hwichan and Chen, Zhousi and Komachi, Mamoru. TMU - HIT `s Submission for the WMT 24 Quality Estimation Shared Task: Is GPT -4 a Good Evaluator for Machine Translation?. 2024. doi:10.18653/v1/2024.wmt-1.38

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HW - TSC 2024 Submission for the Quality Estimation Shared Task

Shan, Weiqiao and Zhu, Ming and Li, Yuang and Piao, Mengyao and Zhao, Xiaofeng and Su, Chang and Zhang, Min and Yang, Hao and Jiang, Yanfei. HW - TSC 2024 Submission for the Quality Estimation Shared Task. 2024. doi:10.18653/v1/2024.wmt-1.39

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HW - TSC `s Participation in the WMT 2024 QEAPE Task

Yu, Jiawei and Zhao, Xiaofeng and Zhang, Min and Yanqing, Zhao and Li, Yuang and Chang, Su and Qiao, Xiaosong and Miaomiao, Ma and Yang, Hao. HW - TSC `s Participation in the WMT 2024 QEAPE Task. 2024. doi:10.18653/v1/2024.wmt-1.40

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The B angla/ B engali Seed Dataset Submission to the WMT 24 Open Language Data Initiative Shared Task

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A High-quality Seed Dataset for I talian Machine Translation

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Correcting FLORES Evaluation Dataset for Four A frican Languages

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Expanding FLORES + Benchmark for More Low-Resource Settings: P ortuguese-Emakhuwa Machine Translation Evaluation

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Enhancing Tuvan Language Resources through the FLORES Dataset

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Machine Translation Evaluation Benchmark for W u C hinese: Workflow and Analysis

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Open Language Data Initiative: Advancing Low-Resource Machine Translation for K arakalpak

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FLORES + Translation and Machine Translation Evaluation for the E rzya Language

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S panish Corpus and Provenance with Computer-Aided Translation for the WMT 24 OLDI Shared Task

Cols, Jose. S panish Corpus and Provenance with Computer-Aided Translation for the WMT 24 OLDI Shared Task. 2024. doi:10.18653/v1/2024.wmt-1.50

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Efficient Terminology Integration for LLM -based Translation in Specialized Domains

Kim, Sejoon and Sung, Mingi and Lee, Jeonghwan and Lim, Hyunkuk and Gimenez Perez, Jorge. Efficient Terminology Integration for LLM -based Translation in Specialized Domains. 2024. doi:10.18653/v1/2024.wmt-1.51

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Rakuten`s Participation in WMT 2024 Patent Translation Task

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The SETU - ADAPT Submission for WMT 24 Biomedical Shared Task

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Findings of WMT 2024 Shared Task on Low-Resource I ndic Languages Translation

Pakray, Partha and Pal, Santanu and Vetagiri, Advaitha and Krishna, Reddi and Maji, Arnab Kumar and Dash, Sandeep and Laitonjam, Lenin and Sarah, Lyngdoh and Manna, Riyanka. Findings of WMT 2024 Shared Task on Low-Resource I ndic Languages Translation. 2024. doi:10.18653/v1/20...

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Findings of WMT 2024`s M ulti I ndic22 MT Shared Task for Machine Translation of 22 I ndian Languages

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Findings of WMT 2024 E nglish-to-Low Resource Multimodal Translation Task

Parida, Shantipriya and Bojar, Ond r ej and Abdulmumin, Idris and Muhammad, Shamsuddeen Hassan and Ahmad, Ibrahim Said. Findings of WMT 2024 E nglish-to-Low Resource Multimodal Translation Task. 2024. doi:10.18653/v1/2024.wmt-1.56

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Findings of the WMT 2024 Shared Task Translation into Low-Resource Languages of S pain: Blending Rule-Based and Neural Systems

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Findings of the WMT 2024 Shared Task on Discourse-Level Literary Translation

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Findings of the WMT 2024 Shared Task on Non-Repetitive Translation

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A3-108 Controlling Token Generation in Low Resource Machine Translation Systems

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S amsung R & D Institute P hilippines @ WMT 2024 I ndic MT Task

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DLUT - NLP Machine Translation Systems for WMT 24 Low-Resource I ndic Language Translation

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SRIB - NMT `s Submission to the I ndic MT Shared Task in WMT 2024

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MTNLP - IIITH : Machine Translation for Low-Resource I ndic Languages

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Exploration of the C ycle GN Framework for Low-Resource Languages

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The SETU - ADAPT Submissions to the WMT 24 Low-Resource I ndic Language Translation Task

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SPRING Lab IITM `s Submission to Low Resource I ndic Language Translation Shared Task

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Machine Translation Advancements of Low-Resource I ndian Languages by Transfer Learning

Wei, Bin and Jiawei, Zheng and Li, Zongyao and Wu, Zhanglin and Guo, Jiaxin and Wei, Daimeng and Rao, Zhiqiang and Li, Shaojun and Luo, Yuanchang and Shang, Hengchao and Yang, Jinlong and Xie, Yuhao and Yang, Hao. Machine Translation Advancements of Low-Resource I ndian Langua...

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NLIP \_ L ab- IITH Low-Resource MT System for WMT 24 I ndic MT Shared Task

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Yes- MT `s Submission to the Low-Resource I ndic Language Translation Shared Task in WMT 2024

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System Description of BV - SLP for S indhi- E nglish Machine Translation in M ulti I ndic22 MT 2024 Shared Task

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WMT 24 System Description for the M ulti I ndic22 MT Shared Task on M anipuri Language

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NLIP -Lab- IITH Multilingual MT System for WAT 24 MT Shared Task

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DCU ADAPT at WMT 24: E nglish to Low-resource Multi-Modal Translation Task

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E nglish-to-Low-Resource Translation: A Multimodal Approach for H indi, M alayalam, B engali, and H ausa

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O dia G en AI `s Participation in WMT 2024 E nglish-to-Low Resource Multimodal Translation Task

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Arewa NLP `s Participation at WMT 24

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Multimodal Machine Translation for Low-Resource I ndic Languages: A Chain-of-Thought Approach Using Large Language Models

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Chitranuvad: Adapting Multi-lingual LLM s for Multimodal Translation

Khan, Shaharukh and Tarun, Ayush and Faraz, Ali and Kamble, Palash and Dahiya, Vivek and Pokala, Praveen and Kulkarni, Ashish and Khatri, Chandra and Ravi, Abhinav and Agarwal, Shubham. Chitranuvad: Adapting Multi-lingual LLM s for Multimodal Translation. 2024. doi:10.18653/v1...

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Brotherhood at WMT 2024: Leveraging LLM -Generated Contextual Conversations for Cross-Lingual Image Captioning

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TIM - UNIGE Translation into Low-Resource Languages of S pain for WMT 24

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TAN - IBE Participation in the Shared Task: Translation into Low-Resource Languages of S pain

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Enhaced Apertium System: Translation into Low-Resource Languages of S pain S panish -- A sturian

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S amsung R & D Institute P hilippines @ WMT 2024 Low-resource Languages of S pain Shared Task

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Back to the Stats: Rescuing Low Resource Neural Machine Translation with Statistical Methods

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Hybrid Distillation from RBMT and NMT : H elsinki- NLP `s Submission to the Shared Task on Translation into Low-Resource Languages of S pain

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Robustness of Fine-Tuned Models for Machine Translation with Varying Noise Levels: Insights for A sturian, A ragonese and Aranese

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Training and Fine-Tuning NMT Models for Low-Resource Languages Using Apertium-Based Synthetic Corpora

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Vicomtech@ WMT 2024: Shared Task on Translation into Low-Resource Languages of S pain

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SJTU System Description for the WMT 24 Low-Resource Languages of S pain Task

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Multilingual Transfer and Domain Adaptation for Low-Resource Languages of S pain

Luo, Yuanchang and Wu, Zhanglin and Wei, Daimeng and Shang, Hengchao and Li, Zongyao and Guo, Jiaxin and Rao, Zhiqiang and Li, Shaojun and Yang, Jinlong and Xie, Yuhao and Jiawei, Zheng and Wei, Bin and Yang, Hao. Multilingual Transfer and Domain Adaptation for Low-Resource La...

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TRIBBLE - TR anslating IB erian languages Based on Limited E -resources

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C loud S heep System for WMT 24 Discourse-Level Literary Translation

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Final Submission of SJTUL ove F iction to Literary Task

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Context-aware and Style-related Incremental Decoding Framework for Discourse-Level Literary Translation

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N ovel T rans: System for WMT 24 Discourse-Level Literary Translation

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L in C hance- NTU for Unconstrained WMT 2024 Literary Translation

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Improving Context Usage for Translating Bilingual Customer Support Chat with Large Language Models

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Optimising LLM -Driven Machine Translation with Context-Aware Sliding Windows

Yang, Xinye and Mu, Yida and Bontcheva, Kalina and Song, Xingyi. Optimising LLM -Driven Machine Translation with Context-Aware Sliding Windows. 2024. doi:10.18653/v1/2024.wmt-1.101

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Context-Aware LLM Translation System Using Conversation Summarization and Dialogue History

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Enhancing Translation Quality: A Comparative Study of Fine-Tuning and Prompt Engineering in Dialog-Oriented Machine Translation Systems

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The SETU - ADAPT Submissions to WMT 2024 Chat Translation Tasks

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Exploring the Traditional NMT Model and Large Language Model for Chat Translation

Yang, Jinlong and Shang, Hengchao and Wei, Daimeng and Guo, Jiaxin and Li, Zongyao and Wu, Zhanglin and Rao, Zhiqiang and Li, Shaojun and Xie, Yuhao and Luo, Yuanchang and Jiawei, Zheng and Wei, Bin and Yang, Hao. Exploring the Traditional NMT Model and Large Language Model fo...

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Graph Representations for Machine Translation in Dialogue Settings

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Reducing Redundancy in J apanese-to- E nglish Translation: A Multi-Pipeline Approach for Translating Repeated Elements in J apanese

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SYSTRAN @ WMT 24 Non-Repetitive Translation Task

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Mitigating Metric Bias in Minimum B ayes Risk Decoding

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Beyond Human-Only: Evaluating Human-Machine Collaboration for Collecting High-Quality Translation Data

Liu, Zhongtao and Riley, Parker and Deutsch, Daniel and Lui, Alison and Niu, Mengmeng and Shah, Apurva and Freitag, Markus. Beyond Human-Only: Evaluating Human-Machine Collaboration for Collecting High-Quality Translation Data. 2024. doi:10.18653/v1/2024.wmt-1.110

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How Effective Are State Space Models for Machine Translation?

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Evaluation and Large-scale Training for Contextual Machine Translation

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A Multi-task Learning Framework for Evaluating Machine Translation of Emotion-loaded User-generated Content

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On Instruction-Finetuning Neural Machine Translation Models

Raunak, Vikas and Grundkiewicz, Roman and Junczys-Dowmunt, Marcin. On Instruction-Finetuning Neural Machine Translation Models. 2024. doi:10.18653/v1/2024.wmt-1.114

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Benchmarking Visually-Situated Translation of Text in Natural Images

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Analysing Translation Artifacts: A Comparative Study of LLM s, NMT s, and Human Translations

Sizov, Fedor and Espa \ n a-Bonet, Cristina and Van Genabith, Josef and Xie, Roy and Dutta Chowdhury, Koel. Analysing Translation Artifacts: A Comparative Study of LLM s, NMT s, and Human Translations. 2024. doi:10.18653/v1/2024.wmt-1.116

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How Grammatical Features Impact Machine Translation: A New Test Suite for C hinese- E nglish MT Evaluation

Song, Huacheng and Li, Yi and Wu, Yiwen and Liu, Yu and Lin, Jingxia and Xu, Hongzhi. How Grammatical Features Impact Machine Translation: A New Test Suite for C hinese- E nglish MT Evaluation. 2024. doi:10.18653/v1/2024.wmt-1.117

2024 doi
[258]

Improving Statistical Significance in Human Evaluation of Automatic Metrics via Soft Pairwise Accuracy

Thompson, Brian and Mathur, Nitika and Deutsch, Daniel and Khayrallah, Huda. Improving Statistical Significance in Human Evaluation of Automatic Metrics via Soft Pairwise Accuracy. 2024. doi:10.18653/v1/2024.wmt-1.118

2024 doi
[259]

Speech Is More than Words: Do Speech-to-Text Translation Systems Leverage Prosody?

Tsiamas, Ioannis and Sperber, Matthias and Finch, Andrew and Garg, Sarthak. Speech Is More than Words: Do Speech-to-Text Translation Systems Leverage Prosody?. 2024. doi:10.18653/v1/2024.wmt-1.119

2024 doi
[260]

Cultural Adaptation of Menus: A Fine-Grained Approach

Zhang, Zhonghe and He, Xiaoyu and Iyer, Vivek and Birch, Alexandra. Cultural Adaptation of Menus: A Fine-Grained Approach. 2024. doi:10.18653/v1/2024.wmt-1.120

2024 doi
[261]

Pitfalls and Outlooks in Using COMET

Zouhar, Vil \'e m and Chen, Pinzhen and Lam, Tsz Kin and Moghe, Nikita and Haddow, Barry. Pitfalls and Outlooks in Using COMET. 2024. doi:10.18653/v1/2024.wmt-1.121

2024 doi
[262]

Post-edits Are Preferences Too

Berger, Nathaniel and Riezler, Stefan and Exel, Miriam and Huck, Matthias. Post-edits Are Preferences Too. 2024. doi:10.18653/v1/2024.wmt-1.122

2024 doi
[263]

Translating Step-by-Step: Decomposing the Translation Process for Improved Translation Quality of Long-Form Texts

Briakou, Eleftheria and Luo, Jiaming and Cherry, Colin and Freitag, Markus. Translating Step-by-Step: Decomposing the Translation Process for Improved Translation Quality of Long-Form Texts. 2024. doi:10.18653/v1/2024.wmt-1.123

2024 doi
[264]

Scaling Laws of Decoder-Only Models on the Multilingual Machine Translation Task

Caillaut, Ga. Scaling Laws of Decoder-Only Models on the Multilingual Machine Translation Task. 2024. doi:10.18653/v1/2024.wmt-1.124

2024 doi
[265]

Shortcomings of LLM s for Low-Resource Translation: Retrieval and Understanding Are Both the Problem

Court, Sara and Elsner, Micha. Shortcomings of LLM s for Low-Resource Translation: Retrieval and Understanding Are Both the Problem. 2024. doi:10.18653/v1/2024.wmt-1.125

2024 doi
[266]

Introducing the N ews P a LM MBR and QE Dataset: LLM -Generated High-Quality Parallel Data Outperforms Traditional Web-Crawled Data

Finkelstein, Mara and Vilar, David and Freitag, Markus. Introducing the N ews P a LM MBR and QE Dataset: LLM -Generated High-Quality Parallel Data Outperforms Traditional Web-Crawled Data. 2024. doi:10.18653/v1/2024.wmt-1.126

2024 doi
[267]

Is Preference Alignment Always the Best Option to Enhance LLM -Based Translation? An Empirical Analysis

Gisserot-Boukhlef, Hippolyte and Rei, Ricardo and Malherbe, Emmanuel and Hudelot, C \'e line and Colombo, Pierre and Guerreiro, Nuno M. Is Preference Alignment Always the Best Option to Enhance LLM -Based Translation? An Empirical Analysis. 2024. doi:10.18653/v1/2024.wmt-1.127

2024 doi
[268]

Quality or Quantity? On Data Scale and Diversity in Adapting Large Language Models for Low-Resource Translation

Iyer, Vivek and Malik, Bhavitvya and Stepachev, Pavel and Chen, Pinzhen and Haddow, Barry and Birch, Alexandra. Quality or Quantity? On Data Scale and Diversity in Adapting Large Language Models for Low-Resource Translation. 2024. doi:10.18653/v1/2024.wmt-1.128

2024 doi
[269]

Efficient Technical Term Translation: A Knowledge Distillation Approach for Parenthetical Terminology Translation

Jiyoon, Myung and Park, Jihyeon and Son, Jungki and Lee, Kyungro and Han, Joohyung. Efficient Technical Term Translation: A Knowledge Distillation Approach for Parenthetical Terminology Translation. 2024. doi:10.18653/v1/2024.wmt-1.129

2024 doi
[270]

Assessing the Role of Imagery in Multimodal Machine Translation

Kashani Motlagh, Nicholas and Davis, Jim and Gwinnup, Jeremy and Erdmann, Grant and Anderson, Tim. Assessing the Role of Imagery in Multimodal Machine Translation. 2024. doi:10.18653/v1/2024.wmt-1.130

2024 doi
[271]

Error Span Annotation: A Balanced Approach for Human Evaluation of Machine Translation

Kocmi, Tom and Zouhar, Vil \'e m and Avramidis, Eleftherios and Grundkiewicz, Roman and Karpinska, Marzena and Popovi \'c , Maja and Sachan, Mrinmaya and Shmatova, Mariya. Error Span Annotation: A Balanced Approach for Human Evaluation of Machine Translation. 2024. doi:10.1865...

2024 doi
[272]

Neural Methods for Aligning Large-Scale Parallel Corpora from the Web for South and E ast A sian Languages

Koehn, Philipp. Neural Methods for Aligning Large-Scale Parallel Corpora from the Web for South and E ast A sian Languages. 2024. doi:10.18653/v1/2024.wmt-1.132

2024 doi
[273]

Plug, Play, and Fuse: Zero-Shot Joint Decoding via Word-Level Re-ranking across Diverse Vocabularies

Koneru, Sai and Huck, Matthias and Exel, Miriam and Niehues, Jan. Plug, Play, and Fuse: Zero-Shot Joint Decoding via Word-Level Re-ranking across Diverse Vocabularies. 2024. doi:10.18653/v1/2024.wmt-1.133

2024 doi
[274]

Proceedings of the Eighth Widening NLP Workshop. 2024. doi:10.18653/v1/2024.winlp-1.0

2024 doi
[275]

Proceedings of the 7th Workshop on Indian Language Data: Resources and Evaluation. 2024

2024
[276]

Towards Disfluency Annotated Corpora for I ndian Languages

Kochar, Chayan and Mujadia, Vandan Vasantlal and Mishra, Pruthwik and Sharma, Dipti Misra. Towards Disfluency Annotated Corpora for I ndian Languages. 2024

2024
[277]

E mo M ix-3 L : A Code-Mixed Dataset for B angla- E nglish- H indi for Emotion Detection

Raihan, Nishat and Goswami, Dhiman and Mahmud, Antara and Anastasopoulos, Antonios and Zampieri, Marcos. E mo M ix-3 L : A Code-Mixed Dataset for B angla- E nglish- H indi for Emotion Detection. 2024

2024
[278]

and Buitelaar, Paul and McCrae, John P

Rani, Priya and Negi, Gaurav and Jha, Saroj and Suryawanshi, Shardul and Ojha, Atul Kr. and Buitelaar, Paul and McCrae, John P. Findings of the WILDRE Shared Task on Code-mixed Less-resourced Sentiment Analysis for I ndo- A ryan Languages. 2024

2024
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Multilingual Bias Detection and Mitigation for I ndian Languages

Maity, Ankita and Sharma, Anubhav and Dhar, Rudra and Abhishek, Tushar and Gupta, Manish and Varma, Vasudeva. Multilingual Bias Detection and Mitigation for I ndian Languages. 2024

2024
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Dharma \'s \= a stra Informatics: Concept Mining System for Socio-Cultural Facet in A ncient I ndia

Nigam, Arooshi and Chandra, Subhash. Dharma \'s \= a stra Informatics: Concept Mining System for Socio-Cultural Facet in A ncient I ndia. 2024

2024
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Exploring News Summarization and Enrichment in a Highly Resource-Scarce I ndian Language: A Case Study of Mizo

Bala, Abhinaba and Urlana, Ashok and Mishra, Rahul and Krishnamurthy, Parameswari. Exploring News Summarization and Enrichment in a Highly Resource-Scarce I ndian Language: A Case Study of Mizo. 2024

2024
[282]

Finding the Causality of an Event in News Articles

Lalitha Devi, Sobha and RK Rao, Pattabhi. Finding the Causality of an Event in News Articles. 2024

2024
[283]

Creating Corpus of Low Resource I ndian Languages for Natural Language Processing: Challenges and Opportunities

Dongare, Pratibha. Creating Corpus of Low Resource I ndian Languages for Natural Language Processing: Challenges and Opportunities. 2024

2024
[284]

FZZG at WILDRE -7: Fine-tuning Pre-trained Models for Code-mixed, Less-resourced Sentiment Analysis

Thakkar, Gaurish and Tadi \'c , Marko and Mikelic Preradovic, Nives. FZZG at WILDRE -7: Fine-tuning Pre-trained Models for Code-mixed, Less-resourced Sentiment Analysis. 2024

2024
[285]

MLI nitiative@ WILDRE 7: Hybrid Approaches with Large Language Models for Enhanced Sentiment Analysis in Code-Switched and Code-Mixed Texts

Veeramani, Hariram and Thapa, Surendrabikram and Naseem, Usman. MLI nitiative@ WILDRE 7: Hybrid Approaches with Large Language Models for Enhanced Sentiment Analysis in Code-Switched and Code-Mixed Texts. 2024

2024
[286]

Aalamaram: A Large-Scale Linguistically Annotated Treebank for the T amil Language

Abirami, A M and Leong, Wei Qi and Rengarajan, Hamsawardhini and Anitha, D and Suganya, R and Singh, Himanshu and Sarveswaran, Kengatharaiyer and Tjhi, William Chandra and Shah, Rajiv Ratn. Aalamaram: A Large-Scale Linguistically Annotated Treebank for the T amil Language. 2024

2024
[287]

Proceedings of the First Workshop on Advancing Natural Language Processing for Wikipedia. 2024. doi:10.18653/v1/2024.wikinlp-1.0

2024 doi
[288]

B ord IR lines: A Dataset for Evaluating Cross-lingual Retrieval Augmented Generation

Li, Bryan and Haider, Samar and Luo, Fiona and Agashe, Adwait and Callison-Burch, Chris. B ord IR lines: A Dataset for Evaluating Cross-lingual Retrieval Augmented Generation. 2024. doi:10.18653/v1/2024.wikinlp-1.3

2024 doi
[289]

Multi-Label Field Classification for Scientific Documents using Expert and Crowd-sourced Knowledge

Gelles, Rebecca and Dunham, James. Multi-Label Field Classification for Scientific Documents using Expert and Crowd-sourced Knowledge. 2024. doi:10.18653/v1/2024.wikinlp-1.7

2024 doi
[290]

Uncovering Differences in Persuasive Language in R ussian versus E nglish W ikipedia

Li, Bryan and Panasyuk, Aleksey and Callison-Burch, Chris. Uncovering Differences in Persuasive Language in R ussian versus E nglish W ikipedia. 2024. doi:10.18653/v1/2024.wikinlp-1.8

2024 doi
[291]

and Srinivasan, Krishna and Clinchant, St \'e phane and Lin, Jimmy

Yang, Jheng-Hong and Lassance, Carlos and Rezende, Rafael S. and Srinivasan, Krishna and Clinchant, St \'e phane and Lin, Jimmy. Retrieval Evaluation for Long-Form and Knowledge-Intensive Image -- Text Article Composition. 2024. doi:10.18653/v1/2024.wikinlp-1.9

2024 doi
[292]

and Lopez-Ponce, Francisco Fernando and Ojeda-Trueba, Sergio-Luis and Bel-Enguix, Gemma

Salas-Jimenez, K. and Lopez-Ponce, Francisco Fernando and Ojeda-Trueba, Sergio-Luis and Bel-Enguix, Gemma. W iki B ias as an Extrapolation Corpus for Bias Detection. 2024. doi:10.18653/v1/2024.wikinlp-1.10

2024 doi
[293]

HOAXPEDIA : A Unified W ikipedia Hoax Articles Dataset

Borkakoty, Hsuvas and Espinosa-Anke, Luis. HOAXPEDIA : A Unified W ikipedia Hoax Articles Dataset. 2024. doi:10.18653/v1/2024.wikinlp-1.11

2024 doi
[294]

The Rise of AI -Generated Content in W ikipedia

Brooks, Creston and Eggert, Samuel and Peskoff, Denis. The Rise of AI -Generated Content in W ikipedia. 2024. doi:10.18653/v1/2024.wikinlp-1.12

2024 doi
[295]

Embedded Topic Models Enhanced by Wikification

Shibuya, Takashi and Utsuro, Takehito. Embedded Topic Models Enhanced by Wikification. 2024. doi:10.18653/v1/2024.wikinlp-1.13

2024 doi
[296]

Wikimedia data for AI : a review of Wikimedia datasets for NLP tasks and AI -assisted editing

Johnson, Isaac and Kaffee, Lucie-Aim \'e e and Redi, Miriam. Wikimedia data for AI : a review of Wikimedia datasets for NLP tasks and AI -assisted editing. 2024. doi:10.18653/v1/2024.wikinlp-1.14

2024 doi
[297]

Blocks Architecture ( B lo A rk): Efficient, Cost-Effective, and Incremental Dataset Architecture for W ikipedia Revision History

Li, Lingxi and Yao, Zonghai and Kwon, Sunjae and Yu, Hong. Blocks Architecture ( B lo A rk): Efficient, Cost-Effective, and Incremental Dataset Architecture for W ikipedia Revision History. 2024. doi:10.18653/v1/2024.wikinlp-1.16

2024 doi
[298]

ARMADA : Attribute-Based Multimodal Data Augmentation

Jin, Xiaomeng and Kim, Jeonghwan and Zhou, Yu and Huang, Kuan-Hao and Wu, Te-Lin and Peng, Nanyun and Ji, Heng. ARMADA : Attribute-Based Multimodal Data Augmentation. 2024. doi:10.18653/v1/2024.wikinlp-1.17

2024 doi
[299]

Summarization-Based Document ID s for Generative Retrieval with Language Models

Li, Alan and Cheng, Daniel and Keung, Phillip and Kasai, Jungo and Smith, Noah A. Summarization-Based Document ID s for Generative Retrieval with Language Models. 2024. doi:10.18653/v1/2024.wikinlp-1.18

2024 doi
[300]

Proceedings of the Eleventh Workshop on Asian Translation (WAT 2024). 2024. doi:10.18653/v1/2024.wat-1.0

2024 doi

Pith tools

Reviewed May 9, 2026 · model on record in the stance chip above.

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