Pith. sign in

REVIEW 4 major objections 6 minor 1 cited by

Enhanced Arabic Text Retrieval with Attentive Relevance Scoring

T0 review · 4 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This paper claims that replacing dot-product relevance with a small trainable Attentive Relevance Scoring module lets a lightweight Arabic DPR model beat AraDPR and other baselines on every top-k cutoff in ArabicaQA.

desk verdict A small, honest Arabic dense retrieval system whose gains look real but whose central attribution to the new scoring module is untested — worth a revision, not a rejection. read the letter →

arxiv 2507.23404 v1 pith:TN5HOCR5 submitted 2025-07-31 cs.CL

Salah Eddine Bekhouche , Azeddine Benlamoudi , Yazid Bounab , Fadi Dornaika , Abdenour Hadid This is my paper
classification cs.CL
keywords ArabicNLPDensePassageRetrievalAttentiveRelevanceScoringquestionansweringArabicaQAWikipediacontrastivelearninglow-resource
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

Arabic dense retrieval usually scores a question against a passage with a single dot product between vector embeddings, a step that is cheap but insensitive to Arabic's morphology and near-synonymy. The paper proposes APR, a Dense Passage Retrieval system that keeps a lightweight Arabic dual encoder and inserts a trainable Attentive Relevance Scoring (ARS) head in place of the fixed dot product. ARS projects both embeddings, combines them with an element-wise interaction and tanh activation, and learns a weight vector that turns the interaction into a relevance score. The authors report that APR beats DPR, AraDPR, BM25, and TF-IDF at every top-k cutoff on ArabicaQA, with the largest absolute gain over AraDPR at Top-10. They attribute the improvement to ARS's ability to model semantic relevance in Arabic, while leaving detailed component ablation to future work.

What carries the argument

The load-bearing object is the Attentive Relevance Scoring module, a scoring head with about 0.13 million parameters. Given $\ell^2$-normalized [CLS] embeddings $q$ and $p$, it computes $h_q = W_q q$, $h_p = W_p p$, then $a = \tanh(h_q \odot h_p)$, and finally $r = \sigma(w_a^\top a)$; this scalar is the ranking score that replaces the dot product. Because passage embeddings are pre-computed, the module only needs the query embedding at inference and can score any number of stored passages. The module is trained jointly with an InfoNCE contrastive loss on the [CLS] embeddings, a dynamic relevance loss that pushes positive scores toward 1 and negative scores toward 0, and a logit-standard-deviation regularizer, weighted by $\alpha=1$, $\beta=1$, and $\gamma=0.1$.

What would settle it

Train APR on the same ArabicaQA split with the ARS head replaced by a plain dot-product scorer while keeping the encoders and all three losses unchanged; if the top-k accuracy at every cutoff stays within noise of the reported Table 1 numbers, the claim that attentive relevance scoring drives the gains is refuted, and likewise rerunning DPR and AraDPR in the identical evaluation harness would test whether the baseline comparison is fair.

Watch

Extended reading notes

Core claim

The paper's central claim is that the final relevance score in Arabic dense retrieval should be learned rather than fixed. APR keeps a lightweight dual-encoder architecture initialized from an Arabic MiniBERT model but replaces dot-product or cosine scoring with the trained Attentive Relevance Scoring module. On the ArabicaQA test set the authors report top-1/10/20/50/100 accuracy of 37.01/63.17/66.36/70.77/73.43 percent, against AraDPR's 36.10/58.40/63.40/68.60/71.90 and DPR's 36.40/57.80/62.10/66.60/69.50. The consistency of the gap, especially +4.77 at top-10, is presented as evidence that ARS separates semantically similar but incorrect passages from truly relevant ones, improving the candidates handed to downstream readers.

Load-bearing premise

The fragile premise is that the Table 1 baseline numbers come from the same ArabicaQA split, Wikipedia passage index, preprocessing, and negative sampling as APR, so the reported gains can be credited to the attentive scoring module rather than to differences in experimental setup.

Editorial extensions

If this is right

  • Downstream Arabic question-answering readers should receive higher-quality candidate passages, with the largest benefit at Top-10 where the reported gain over AraDPR is +4.77 percent.
  • Because the ARS head adds only 0.13 million parameters, the ranking improvement does not require a larger or heavier encoder, which is useful for low-resource deployments of Arabic retrieval.
  • Since passage embeddings remain pre-computable, ARS can be added as a cheap reranking step over an approximate-nearest-neighbor shortlist without re-encoding the corpus.
  • The consistent lead at every k value suggests the method improves overall ranking quality rather than only the first retrieved item.

Reading between the lines

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

  • If ARS is the true driver, the same two-vector interaction head should transfer to other morphologically rich, under-resourced languages by swapping in a suitable pretrained encoder, with the largest gains expected where dot-product similarity is a weak relevance proxy.
  • The reported system combines ARS with two auxiliary losses, so the contribution of the scoring head alone is not yet isolated; a natural test is to fix the encoders and losses and compare dot-product scoring with ARS scoring.
  • A practical two-stage retrieval design follows directly from the architecture: use ANN search with the CLS embeddings to obtain candidates quickly, then re-rank them with ARS, combining fast recall with finer semantic scoring.
  • If the dynamic relevance loss keeps scores well separated on hard negatives, ARS scores may also serve as confidence estimates, allowing Arabic QA systems to abstain when no passage scores highly.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper proposes APR, an Arabic dense passage retrieval model built on dual MiniBERT encoders and a trainable Attentive Relevance Scoring (ARS) module that replaces dot-product scoring with a learned interaction layer (Eqs. 2–4). Training combines an InfoNCE contrastive loss with a dynamic relevance loss and a logit regularization term (Eqs. 5–8). The authors evaluate on ArabicaQA and report top-k retrieval accuracy (Table 1) showing APR above TF-IDF, BM25, DPR, and AraDPR at all cutoffs. The central claim is that the ARS module is responsible for these improvements, but the paper provides no ablation that isolates ARS or the auxiliary losses, and the baseline comparison setup is not documented in enough detail to rule out confounds.

Significance. If the central claim were established, a lightweight learned relevance scoring function that improves Arabic retrieval over standard dot-product DPR would be a useful contribution, particularly for morphologically rich and low-resource languages. The paper has positive elements: the evaluation is on a held-out test split (so the scoring is not circular), the model adds only 0.13M parameters over the dual encoders, and the code is promised publicly. However, the causal role of the ARS module is not tested, baseline comparability is undocumented, no statistical reliability is reported, and there are numeric inconsistencies in the results section. These issues currently prevent the paper from supporting its main claim.

major comments (4)
  1. [Section 5 and Section 4.3] The central claim that the ARS module drives the observed gains is not tested. APR differs from the DPR and AraDPR baselines in at least four ways: the encoder initialization (MiniBERT vs. AraBERT), the ARS scoring function in Eqs. (2)–(4), the dynamic relevance loss L_dyn in Eq. (7), and the logit regularization L_reg in Eq. (8). No ablation configuration removes ARS while holding the other components fixed, and the paper explicitly defers 'thorough ablation studies' to future work in Section 5. Consequently, the statement in Section 4.3 that the results 'demonstrate that APR effectively leverages the ARS module' is an attribution without a control. This is a missing-support issue for the paper's main contribution, not merely a question of presentation.
  2. [Table 1 and Section 4.2] The comparability of the baseline numbers is not documented. The paper does not state whether the DPR and AraDPR rows were produced in the same harness as APR, with the same ArabicaQA train/validation/test splits, the same Arabic Wikipedia passage index, the same preprocessing, and the same negative sampling. If those rows are quoted from prior papers under different evaluation conditions, the reported gains are not apples-to-apples and the superiority claim in Section 4.3 is not established. Please either rerun all baselines under identical settings or provide explicit provenance and settings for each row, including the exact passage corpus and retrieval procedure (e.g., exhaustive search vs. ANN).
  3. [Section 4.3, text vs. Table 1] The results section contains numeric inconsistencies with Table 1. The text says APR achieves 'about 38%' at k = 5 and cites AraDPR (36.10%) and DPR (36.40%) at that same cutoff, but those two baseline values are the Top-1 entries in Table 1, not k = 5 values. The text also states APR reaches 75.01% at k = 100, whereas Table 1 reports 73.43. These discrepancies must be resolved. Additionally, the caption of Figure 2 should clarify whether it shows validation or test accuracy and what the x-axis represents, since the discussion draws conclusions from these numbers.
  4. [Table 1 and Section 4.2] No error bars, multiple-seed results, or statistical significance tests are reported for any method. The Top-1 gain over AraDPR is only +0.91 percentage points, which may be within run-to-run noise. Without at least a few random seeds or a paired significance test, the claim that APR 'outperforms all baseline systems across all top-k retrieval thresholds' is not statistically supported, even setting aside the ablation issue. Please report variance across seeds or a significance test.
minor comments (6)
  1. [Section 3, paragraph 1] Typo: 'the overall architecture of our pro APR' should read 'the overall architecture of our proposed APR'.
  2. [Section 4.3, paragraph 3] The phrase 'Answer Relevance Scoring' should be 'Attentive Relevance Scoring' to match the method name used elsewhere in the paper.
  3. [Reference [25]] Reference [25] is cited as the source of 'MiniBERT', but the listed reference describes 'KUISAIL at SemEval-2020 Task 12: Bert-CNN for offensive speech identification in social media'. The connection to MiniBERT is not evident and the citation appears incorrect; please provide the correct reference for the Arabic MiniBERT model or specify the model version and source.
  4. [Section 3.2, Eqs. (2)–(4)] The term 'attention' for a single learned weight vector w_a applied to an element-wise product is unconventional. If this is intended as an attention mechanism, the paper should clarify how attention weights are derived; if it is a learned interaction layer, the discussion should position it relative to existing interaction-based rankers (e.g., DSSM or attentive matching).
  5. [Section 4.2] The training description does not state the number of epochs or total training steps, nor which negative passage is used in L_dyn (Eq. 7) when multiple negatives are available. Adding these details would improve reproducibility.
  6. [Section 4.1] The superscripts '1' and '2' after 'ArabicaQA dataset' and 'Arabic Wikipedia' appear to refer to dataset URLs or footnotes, but these are not expanded in the bibliography; please add formal citations or a footnote with the dataset links.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: APR is evaluated on a held-out external benchmark, and no claimed gain reduces by construction to the training inputs.

full rationale

The paper's derivation chain is empirical, not definitional. APR is trained on the ArabicaQA training split (58,727 question-answer pairs) and evaluated on the held-out test split (12,597 questions) against the full Arabic Wikipedia passage collection. The retrieval metric is top-k accuracy computed from ARS scores r(j) for unseen queries, so the evaluation is genuinely external. Although Ldyn (Eq. 7) directly supervises the ARS scores r that are also used for ranking, this is a standard supervised ranking objective and does not make the evaluation circular: the test-set scores are not inputs to training, and the model is not 'predicting' its own fitted values. The paper's attribution of the gains to the ARS module is weakened by the absence of ablations (Section 5 explicitly defers them to future work), and the baseline comparability is not fully documented, but these are missing-evidence and experimental-control concerns, not circularity. There are no load-bearing self-citations, no imported uniqueness theorems from the authors' prior work, and no ansatz smuggled in via citation. The paper is self-contained against an external benchmark, so the appropriate circularity score is 0.

Assumptions & free parameters 6 free parameters · 3 assumptions · 0 invented entities

The method rests on standard deep learning machinery plus three unevidenced domain premises: MiniBERT is a suitable Arabic encoder, ArabicaQA negatives are correctly labeled hard negatives, and baseline results are protocol-comparable. All of these are asserted but not independently established in the paper.

free parameters (6)
  • Loss weights alpha, beta, gamma = alpha=1, beta=1, gamma=0.1
    Set empirically in Eq. (5); no sensitivity analysis is reported.
  • Softmax temperature tau = learnable, final value not reported
    Learnable scaling in the InfoNCE loss, Eq. (6); no learned value is given.
  • ARS hidden dimension h
    Dimension of projection matrices Wq and Wp in Eq. (2) is never stated.
  • Batch size and number of negatives = batch size 32 per GPU, N=29
    Selected from the dataset and hardware setup; no ablation on these choices.
  • Learning rate and scheduler = 1e-4, linear warmup from 0.1x
    Chosen without reported tuning or comparison of schedules.
  • Numerical stability constant epsilon = 1e-8
    Small constant added in Ldyn, Eq. (7); standard but still a chosen value.
assumptions (3)
  • domain assumption MiniBERT, as used here, is a transformer pre-trained on Arabic corpora and is suitable as a DPR encoder.
    Section 3.1 states the encoders are initialized with MiniBERT weights, but the cited reference [25] is a SemEval system description for offensive language detection and does not document the MiniBERT Arabic pretraining corpus or checkpoint. If the encoder is not Arabic-pretrained, the Arabic-specific benefit is unsupported.
  • domain assumption ArabicaQA hard negatives are semantically similar to the question and do not contain the answer.
    Section 4.1 relies on the dataset description; if negatives leak answers or are not hard, contrastive training and the reported rankings would be inflated.
  • domain assumption Baseline DPR and AraDPR numbers in Table 1 were produced under the same split, Wikipedia index, preprocessing, and evaluation protocol as APR.
    Section 4.2 gives no detail on rerunning baselines. If the numbers are quoted from external papers with different evaluation details, the gains attributed to APR are not controlled.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Enhanced Arabic Text Retrieval with Attentive Relevance Scoring." pith.science (2026). https://pith.science/paper/TN5HOCR5

@misc{pith2026250723404,
  author       = {Pith},
  title        = {Pith review of: Enhanced Arabic Text Retrieval with Attentive Relevance Scoring},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TN5HOCR5}},
  note         = {Machine review of arXiv:2507.23404}
}
read the original abstract

Arabic poses a particular challenge for natural language processing (NLP) and information retrieval (IR) due to its complex morphology, optional diacritics and the coexistence of Modern Standard Arabic (MSA) and various dialects. Despite the growing global significance of Arabic, it is still underrepresented in NLP research and benchmark resources. In this paper, we present an enhanced Dense Passage Retrieval (DPR) framework developed specifically for Arabic. At the core of our approach is a novel Attentive Relevance Scoring (ARS) that replaces standard interaction mechanisms with an adaptive scoring function that more effectively models the semantic relevance between questions and passages. Our method integrates pre-trained Arabic language models and architectural refinements to improve retrieval performance and significantly increase ranking accuracy when answering Arabic questions. The code is made publicly available at \href{https://github.com/Bekhouche/APR}{GitHub}.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. CVPD at QIAS 2025 Shared Task: An Efficient Encoder-Based Approach for Islamic Inheritance Reasoning

    cs.CL 2025-08 conditional novelty 4.0 of 10

    An encoder-based relevance-scoring system achieves 69.87% accuracy on Islamic inheritance multiple-choice questions, below Gemini's 87.60% but with far smaller compute.

Reference graph

Works this paper leans on

32 extracted references · 25 canonical work pages · cited by 1 Pith paper

  1. [1]

    Applying Dense Passage Retrieval (DPR) [1] to Arabic opens new avenues but also introduces unique challenges

    INTRODUCTION Arabic, one of the most widely spoken languages globally, presents unique linguistic challenges for natural language processing (NLP) and information retrieval (IR). Applying Dense Passage Retrieval (DPR) [1] to Arabic opens new avenues but also introduces unique challenges. Its rich mor- phology, frequent use of diacritics, syntactic complex...

    arXiv 2025

  2. [2]

    A key development stemming from this is DPR, which has become a corner- stone for modern open-domain Question Answering (QA) and search systems

    RELA TED WORK The field of IR has been significantly transformed by ad- vancements in deep learning, particularly the emergence of pre-trained transformer models [3, 4]. A key development stemming from this is DPR, which has become a corner- stone for modern open-domain Question Answering (QA) and search systems. The core idea of DPR, popularized by [1], ...

    work page

  3. [3]

    PROPOSED APPROACH This section introduces our proposed method, called Adaptive Passage Retrieval (APR), tailored for Arabic text retrieval. Our approach builds upon the DPR framework [24] and incorporates a lightweight, Arabic-specific encoder (MiniB- ERT) [25] alongside a novel scoring mechanism termed Attentive Relevance Scoring (ARS). This combination ...

    work page

  4. [4]

    EXPERIMENTS 4.1. Dataset For our experiments, we used the ArabicaQA dataset, a com- prehensive, human-annotated Arabic question answering cor- pus specifically designed for open-domain retrieval and ma- chine reading comprehension tasks. The dataset 1 is divided into standard training, validation and test subsets. The train- ing set consists of 58,727 que...

    work page

  5. [5]

    CONCLUSION We presented an improved dense retrieval framework tailored for Arabic question answering. By integrating a lightweight encoder and ARS, our approach addresses key challenges in Arabic IR, including morphological complexity and limited semantic generalization in traditional models. This work opens new directions for efficient and accurate retri...

    work page

  6. [6]

    Dense passage retrieval for open-domain question answer- ing,

    Vladimir Karpukhin, Barlas O ˘guz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih, “Dense passage retrieval for open-domain question answer- ing,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020, pp. 6769–6781, Association for Computational Linguistics

    work page 2020

  7. [7]

    Arabicaqa: A comprehensive dataset for arabic question answering,

    Abdelrahman Abdallah, Mahmoud Kasem, Mahmoud Ab- dalla, Mohamed Mahmoud, Mohamed Elkasaby, Yasser El- bendary, and Adam Jatowt, “Arabicaqa: A comprehensive dataset for arabic question answering,” in Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2024, pp. 2049–2059

    work page 2024

  8. [8]

    Bert: Pre-training of deep bidirectional transform- ers for language understanding,

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova, “Bert: Pre-training of deep bidirectional transform- ers for language understanding,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technolo- gies, V olume 1 (Long and Short Papers). 2019, pp. 4171–4186, ...

    work page 2019

Show all 32 references
  1. [9]

    Attention is all you need,

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszko- reit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin, “Attention is all you need,” Advances in neural information processing systems, vol. 30, 2017

    2017

  2. [10]

    Billion-scale similarity search with gpus,

    Jeff Johnson, Matthijs Douze, and Herv ´e J´egou, “Billion-scale similarity search with gpus,” IEEE Transactions on Big Data , vol. 7, no. 3, pp. 535–547, 2019

    2019

  3. [11]

    The probabilistic relevance framework: Bm25 and beyond,

    Stephen Robertson, Hugo Zaragoza, et al., “The probabilistic relevance framework: Bm25 and beyond,” F oundations and Trends® in Information Retrieval , vol. 3, no. 4, pp. 333–389, 2009

    2009

  4. [12]

    Approximate nearest neighbor negative contrastive learning for dense text retrieval,

    Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Lau, Jimmy Lin, and Paul Bennett, “Approximate nearest neighbor negative contrastive learning for dense text retrieval,” 2020

    2020

  5. [13]

    RocketQA: An optimized training approach to dense passage retrieval for open-domain question answering,

    Yingqi Qu, Yujing Ding, Jing Liu, Kai Liu, Ruiyang Ren, Wayne Xin Zhao, Daxiang Dong, Hua Wu, and Haifeng Wang, “RocketQA: An optimized training approach to dense passage retrieval for open-domain question answering,” inProceedings of the 2021 Conference of the North American ...

    2021

  6. [14]

    Colbert: Efficient and ef- fective passage search via contextualized late interaction over bert,

    Omar Khattab and Matei Zaharia, “Colbert: Efficient and ef- fective passage search via contextualized late interaction over bert,” in Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Re- trieval, 2020, pp. 39–48

    2020

  7. [15]

    Colbertv2: Effective and effi- cient retrieval via lightweight late interaction,

    Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, Christo- pher Potts, and Matei Zaharia, “Colbertv2: Effective and effi- cient retrieval via lightweight late interaction,” arXiv preprint arXiv:2112.01488, 2021

    2021 arXiv

  8. [16]

    Splade: Sparse lexical and expansion model for first stage ranking,

    Thibault Formal, Benjamin Piwowarski, and St ´ephane Clin- chant, “Splade: Sparse lexical and expansion model for first stage ranking,” in Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Informa- tion Retrieval, 2021

    2021

  9. [17]

    Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter,

    Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf, “Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter,” arXiv preprint arXiv:1910.01108, 2019

    1910 arXiv

  10. [18]

    Tinybert: Distill- ing bert for natural language understanding,

    Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, and Qun Liu, “Tinybert: Distill- ing bert for natural language understanding,” arXiv preprint arXiv:1909.10351, 2019

    1909 arXiv

  11. [19]

    A survey of arabic question answering: challenges, tasks, approaches, tools, and future trends,

    Ahmed Magdy Ezzeldin and Mohamed Shaheen, “A survey of arabic question answering: challenges, tasks, approaches, tools, and future trends,” in Proceedings of The 13th inter- national Arab conference on information technology (ACIT 2012), 2012, pp. 1–8

    2012

  12. [20]

    Arabert: Transformer-based model for arabic language understanding,

    Wissam Antoun, Fady Baly, and Hazem Hajj, “Arabert: Transformer-based model for arabic language understanding,” arXiv preprint arXiv:2003.00104, 2020

    2003 arXiv

  13. [21]

    Arbert & marbert: Deep bidirectional transformers for arabic,

    Muhammad Abdul-Mageed, AbdelRahim Elmadany, and El Moatez Billah Nagoudi, “Arbert & marbert: Deep bidirectional transformers for arabic,” arXiv preprint arXiv:2101.01785, 2020

    2020 arXiv

  14. [22]

    Araelectra: Pre- training text discriminators for arabic language understand- ing,

    Wissam Antoun, Fady Baly, and Hazem Hajj, “Araelectra: Pre- training text discriminators for arabic language understand- ing,” arXiv preprint arXiv:2012.15516, 2020

    2012 arXiv

  15. [23]

    The interplay of variant, size, and task type in arabic pre-trained language models,

    Go Inoue, Bashar Alhafni, Nurpeiis Baimukan, Houda Bouamor, and Nizar Habash, “The interplay of variant, size, and task type in arabic pre-trained language models,” arXiv preprint arXiv:2103.06678, 2021

    2021 arXiv

  16. [24]

    Arabic information retrieval,

    Kareem Darwish, Walid Magdy, et al., “Arabic information retrieval,” F oundations and Trends® in Information Retrieval, vol. 7, no. 4, pp. 239–342, 2014

    2014

  17. [25]

    Arquad: An expert-annotated arabic machine reading comprehension dataset,

    Rasha Obeidat, Marwa Al-Harbi, Mahmoud Al-Ayyoub, and Luay Alawneh, “Arquad: An expert-annotated arabic machine reading comprehension dataset,” Cognitive Computation, vol. 16, no. 3, pp. 984–1003, 2024

    2024

  18. [26]

    Tydi qa: A benchmark for information-seeking question an- swering in ty pologically di verse languages,

    Jonathan H Clark, Eunsol Choi, Michael Collins, Dan Garrette, Tom Kwiatkowski, Vitaly Nikolaev, and Jennimaria Palomaki, “Tydi qa: A benchmark for information-seeking question an- swering in ty pologically di verse languages,” Transactions of the Association for Computational ...

    2020

  19. [27]

    Learning deep structured semantic models for web search using clickthrough data,

    Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck, “Learning deep structured semantic models for web search using clickthrough data,” in Proceed- ings of the 22nd ACM international conference on Information & Knowledge Management, 2013, pp. 2333–2338

    2013

  20. [28]

    Faq retrieval using atten- tive matching,

    Sparsh Gupta and Vitor R Carvalho, “Faq retrieval using atten- tive matching,” in Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Informa- tion Retrieval, 2019, pp. 929–932

    2019

  21. [29]

    Dense passage retrieval for open-domain question an- swering.,

    Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick SH Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih, “Dense passage retrieval for open-domain question an- swering.,” in EMNLP (1), 2020, pp. 6769–6781

    2020

  22. [30]

    Kuisail at semeval-2020 task 12: Bert-cnn for offensive speech iden- tification in social media,

    Ali Safaya, Moutasem Abdullatif, and Deniz Yuret, “Kuisail at semeval-2020 task 12: Bert-cnn for offensive speech iden- tification in social media,” arXiv preprint arXiv:2007.13184 , 2020

    2020 arXiv

  23. [31]

    Decoupled weight decay regularization,

    Ilya Loshchilov and Frank Hutter, “Decoupled weight decay regularization,” in International Conference on Learning Rep- resentations (ICLR), 2019

    2019

  24. [32]

    Neural arabic question answering,

    Hussein Mozannar, Karl El Hajal, Elie Maamary, and Hazem Hajj, “Neural arabic question answering,” arXiv preprint arXiv:1906.05394, 2019

    1906 arXiv

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.