Pith. sign in

REVIEW 2 major objections 4 minor 2 cited by

Analyzing the Effect of Linguistic Similarity on Cross-Lingual Transfer: Tasks and Experimental Setups Matter

T0 review · 2 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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

Pith's one-line read Across 263 languages, the best predictor of zero-shot transfer is task-dependent: syntax for parsing, trigram overlap for n-gram topic models, mBERT coverage for mBERT models.

desk verdict Solid empirical paper; the task-dependence claim survives the missingness concern, though the effect weakens for low-resource targets. read the letter →

arxiv 2501.14491 v3 pith:XSCNA4C6 submitted 2025-01-24 cs.CL

Verena Blaschke , Masha Fedzechkina , Maartje ter Hoeve This is my paper · ORCID
classification cs.CL
keywords cross-lingualtransferlinguisticsimilaritysourcelanguageselectiondependencyparsingPOStaggingtopicclassificationzero-shotmBERTpretrainingcoverage
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

The paper asks a practical question: when building a zero-shot NLP system for a low-resource language, how should you choose the language to train on? It answers with a large-scale measurement: across 263 languages and three tasks, the similarity measure that predicts transfer performance depends on the task and on how the input is represented. Syntactic similarity is the strongest guide for POS tagging and dependency parsing; string and lexical similarity lead for n-gram-based topic classification; and for topic classification with multilingual transformer embeddings, no similarity measure is strong, and what matters most is whether the target language was in the model's pretraining data. If these findings hold, source-language selection should be task-aware rather than based on a single all-purpose language distance. The paper also shows that when no task-specific results exist, borrowing the best source from a conceptually similar experiment is a workable fallback.

What carries the argument

The load-bearing device is a paired comparison between two matrices: a matrix of zero-shot transfer scores for every source-target language pair, and a series of pairwise similarity matrices computed for the same language pairs. Similarity is quantified with Gower's coefficient, a feature-wise agreement score that ignores missing values, applied to typological feature vectors, plus lexical and phylogenetic distances, geographic distance, and dataset-level character, word, trigram, and subword overlap. The analysis ranks similarity measures by their per-test-language Pearson correlation with transfer scores, then reruns the ranking in a source-selection simulation where the most similar language per measure is chosen as the training language and the loss against the best possible source is measured. That simulation turns the correlation result into an actionable heuristic.

What would settle it

A decisive check is to rerun the exact source-selection heuristic on a held-out set of language families with complete typological feature coverage and to see whether the winner remains syntactic similarity for parsing and trigram overlap for n-gram topic classification; if the ranking flips, the task-dependence claim is an artifact of the included languages.

Watch

Extended reading notes

Core claim

The paper's central discovery is that the ranking of linguistic-similarity measures as predictors of zero-shot transfer changes with the task and the input representation. For dependency parsing, the strongest predictor is syntactic similarity, with an average Pearson correlation of 0.57 against labeled attachment score, followed by typological-feature similarity at 0.42; for POS tagging the same syntactic measure leads at 0.37. For n-gram-based topic classification, character trigram overlap is the strongest predictor in the original-script setup (0.65), with lexical similarity close behind, while in the transliterated setup lexical similarity leads (0.61); for topic classification built on multilingual transformer embeddings, no similarity measure exceeds 0.27 and performance instead tracks whether source and target languages appear in the model's pretraining data (68.3% average accuracy when both do, 33.2% when neither does). The practical corollary, demonstrated by a source-selection simulation, is that choosing a source by the task-appropriate measure yields small performance losses, and choosing by the results of a conceptually similar experiment is a safe fallback.

Load-bearing premise

The rankings assume that languages missing from typological databases are missing at random; if entire families or scripts are underrepresented in those databases, the measured correlations between similarity and transfer could be biased.

Editorial extensions

If this is right

  • For POS tagging and dependency parsing, practitioners should rank candidate source languages by syntactic similarity; doing so produced near-best transfer performance among the heuristics tested.
  • For n-gram-based topic classification, ranking sources by character trigram overlap or lexical similarity is the better heuristic, while for transformer-embedding topic classification the source and target should be checked against the model's pretraining coverage.
  • If no task-specific experiment is available, using transfer results from a conceptually similar task with similar input representations yields small losses, but using mismatched representations is a poor guide.
  • Transfer patterns for the two grammatical tasks are highly correlated with each other, so conclusions from one grammatical task are likely to carry over to the other, while the boundary between grammatical and topic-classification tasks matters.
  • Training dataset size and phonological or phonetic similarity were weak predictors across experiments, so they can be deprioritized in source selection for these tasks.

Reading between the lines

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

  • Beyond the paper, the result implies that a single all-purpose language similarity score cannot serve every NLP task; practical tooling should expose separate similarity layers so users can match the measure to their task and model family.
  • The winning role of pretraining coverage for transformer-based topic classification suggests that a tokenizer-exposure index, computed directly from the model's vocabulary, might predict transfer for other frozen multilingual encoders and could be tested on the paper's own 194-language matrix.
  • A natural extension the authors do not run is to check whether the winning measure changes with model scale or architecture, which would show whether the task-dependence they find is tied to mBERT-style tokenization or is a general property of cross-lingual transfer.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 4 minor

Summary. The paper studies zero-shot cross-lingual transfer across 263 languages from 33 families for three tasks: POS tagging, dependency parsing, and topic classification. It computes several linguistic and dataset-based similarity measures (Grambank, lang2vec syntactic/phonological/phonetic, lexical, phylogenetic, geographic, character/word/trigram overlap) and correlates them with transfer performance at the test-language level. The central claim is that the most predictive similarity measure is not universal: syntactic similarity is most predictive for POS tagging and dependency parsing, trigram overlap is most predictive for n-gram-based topic classification, and mBERT pretraining coverage dominates for mBERT-based topic classification. The paper also derives practical implications for source-language selection. The analyses include per-test-language correlations with confidence intervals (Appendix E.3), mixed-effects models (Appendix E.2), and comparisons across tasks and input representations.

Significance. If the findings hold, they have practical value for source-language selection in multilingual NLP, and they clarify contradictions in prior work by showing that task and input representation condition which similarity measure is the best predictor. The study's scale (263 languages, three tasks, multiple similarity measures, explicit handling of correlations between measures) is a strong point, and the paper is transparent about its data sources, exclusions, and limitations. The main descriptive result is supported by a substantial empirical apparatus, including per-test-language correlations and confidence intervals, which is more rigorous than much prior work in this area. The paper also makes a useful methodological contribution by comparing dataset-dependent and dataset-independent similarity measures in a unified framework.

major comments (2)
  1. [§3.3, Table 13, Figure 3] The central claim that syntactic similarity (syn) is the most predictive measure for POS tagging and dependency parsing is load-bearing for the paper's task-dependence conclusion, but it may be driven by the resource-rich subset of languages. Appendix E.3 (Table 13) shows that the mean LAS correlation with syn is 0.69 for test languages in mBERT's pretraining data but only 0.36 for test languages outside it; for POS the corresponding figures are 0.46 and 0.20. Because syn coverage in lang2vec averages only 63% and missingness is likely correlated with language documentation/resource level, the per-test-language correlations for low-resource targets are not only weaker but may be non-representative. The paper should either provide a robustness analysis using imputed features or a high-coverage subset of languages, or explicitly restrict the claim to languages covered by mBERT and discuss the implications for low-resource target languages. Without this, the ranking in Figure 3 may not generalize to the practical setting the paper addresses.
  2. [§E.2, Table 11] The mixed-effects model is the only multivariate analysis that separates syn from correlated predictors such as phylogenetic relatedness, lexical similarity, and mBERT coverage, but it is fit on only 19 training and 31 test languages for the grammatical tasks (42 for topic classification) because of missing data. This is a small, non-representative subset, and the model still exhibits substantial collinearity between gen and lex and between gb and syn (correlations between -0.769 and -0.819 for gen-lex and between -0.442 and -0.509 for gb-syn). I acknowledge the paper's statement that significance values are based on model comparison, which is a reasonable approach, but the generalization of the multivariate conclusions to the full language set is questionable. Adding a sensitivity analysis on a more complete (even if smaller) set of languages with complete feature coverage, or a careful discussion of how missingness could affect the mixed-effects estimates, would substantially strengthen the paper.
minor comments (4)
  1. [§4.1] In the sentence 'If we consider our own POS tagging results but only select the subset of languages that was used in de de Vries et al.'s experiments', there is a duplicated 'de'; it should read 'de Vries et al.'s experiments'.
  2. [§5.1, Figure 3] The label 'word*' in Figure 3 is defined only in the caption as 'overlap between words (wor; UD tasks), trigrams (tri; topics-base/translit), and subword tokens (swt; topics-mbert)'. Consider adding the mapping directly in the figure or a more explicit legend, since the current notation may confuse readers.
  3. [§5.2.1, Table 3] The source-selection analysis would benefit from a random-source baseline. Without knowing the average performance loss when picking a source language uniformly at random, it is difficult to calibrate whether a given loss is 'small' or 'adequate'. The relative ordering of measures is clear, but the practical claim about absolute adequacy is not fully quantified.
  4. [§5.1] The paper treats correlation coefficients with p-values of at least 0.05 as zero. This is a conservative choice, but reporting the raw p-values or providing a significance asterisk in the main figures would make the analysis more transparent, especially for readers who want to assess borderline correlations.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's claims rest on external similarity databases and independently trained transfer models, not on self-referential definitions or fitted inputs.

full rationale

The central claim is that which similarity measure best predicts zero-shot cross-lingual transfer depends on the task and input representation. The similarity measures (Grambank, lang2vec, ASJP, Glottolog, character/word overlap, mBERT pretraining coverage) are external to the transfer models: UDPipe 2 models are pretrained and used as-is, and the topic-classification MLPs are trained on fixed input representations. Nothing is fitted to the target transfer scores and then renamed as a prediction. The high correlation between trigram overlap and n-gram-based topic classification is explicitly acknowledged by the authors as expected from the input representation ('This is expected based on their input representations'), so it is an interpretive observation rather than a circular derivation. The mixed-effects model uses a reduced language set because of missing typological features, which is a data-coverage limitation and a potential external-validity concern, not a circularity. The paper also validates against de Vries et al.'s external POS tagging results (r = 0.73), providing an independent anchor. No load-bearing self-citation, imported uniqueness theorem, or ansatz-smuggling-by-citation appears; the derivation chain is an empirical correlation analysis, not a formal derivation from assumed outputs.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The central claim rests on external data resources rather than on newly fitted parameters. The main ledger entries are domain assumptions about the validity of typological similarity measures and about the representativeness of the three selected tasks. No invented entities are introduced. One set of MLP hyperparameters is grid-searched on a subset of languages, but these do not drive the qualitative task-dependence finding.

free parameters (1)
  • Topic MLP hyperparameters (n-gram and mBERT variants) = Grid-searched on 11 languages; final values in Table 9
    Chosen by development-set performance; they affect absolute accuracy but are not the substrate of the similarity-correlation findings.
assumptions (3)
  • domain assumption The typological feature databases (Grambank, lang2vec, ASJP, Glottolog) provide meaningful and sufficiently complete characterizations of the included languages, and ignoring missing feature values does not bias pairwise similarities.
    Invoked throughout Section 3.3. This is the weakest assumption because if missingness correlates with language families or resource levels, the correlation rankings in Section 5.1 could change.
  • domain assumption Cross-lingual transfer performance is adequately measured by the selected models and datasets (UDPipe 2 for POS/parsing; MLPs on SIB-200 for topic classification).
    The paper uses one model per task and acknowledges that findings might not hold for other models, which limits generalization of the practical recommendations.
  • standard math The correlation and mixed-effects analyses treat language-level averages as meaningful observations, with random effects for source and target languages accounting for some structure.
    Used in Section 5.1 and Appendix E.2. This assumes the mixed model is correctly specified and that treating nonsignificant correlations as zero does not distort averages.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Analyzing the Effect of Linguistic Similarity on Cross-Lingual Transfer: Tasks and Experimental Setups Matter." pith.science (2026). https://pith.science/paper/XSCNA4C6

@misc{pith2026250114491,
  author       = {Pith},
  title        = {Pith review of: Analyzing the Effect of Linguistic Similarity on Cross-Lingual Transfer: Tasks and Experimental Setups Matter},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XSCNA4C6}},
  note         = {Machine review of arXiv:2501.14491}
}
read the original abstract

Cross-lingual transfer is a popular approach to increase the amount of training data for NLP tasks in a low-resource context. However, the best strategy to decide which cross-lingual data to include is unclear. Prior research often focuses on a small set of languages from a few language families and/or a single task. It is still an open question how these findings extend to a wider variety of languages and tasks. In this work, we analyze cross-lingual transfer for 263 languages from a wide variety of language families. Moreover, we include three popular NLP tasks: POS tagging, dependency parsing, and topic classification. Our findings indicate that the effect of linguistic similarity on transfer performance depends on a range of factors: the NLP task, the (mono- or multilingual) input representations, and the definition of linguistic similarity.

Figures

Figures reproduced from arXiv: 2501.14491 by the authors.

Figure 1
Figure 1. Languages included in our experiments. Green indicates languages included in all tasks, blue languages only used for POS tagging and parsing, and purple languages only used for topic classification. Base map via naturalearthdata.com (CC0). for cross-lingual transfer, despite previous work focusing on this question (see §2): earlier studies often contradict each other, and therefore leave a number of important avenue… view at source ↗
Figure 2
Figure 2. Different experiments produce different transfer patterns. NLP transfer results for all combi￾nations of training (columns) and test languages (rows). The darker a cell, the higher the score. The three heatmaps for the grammatical tasks are sorted in the same order, and the three heatmaps with the topic classi￾fication results are sorted in the same order. The darker diagonal shows the within-language scores. Large,… view at source ↗
Figure 3
Figure 3. Mean correlation scores between task re￾sults and similarity measures. “Word*” = overlap between words (wor; UD tasks) trigrams (tri; topics￾base/translit), and subword tokens (swt; topics￾mbert). Dotted lines are added for intelligibility. The strongest predictor for parsing perfor￾mance is syntactic similarity (syn) as determined by lang2vec (ravg_syn= 0.57), followed by the sim￾ilarity of Grambank features (gb; r… view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: Left: Relationship between phylogenetic and syntactic similarity – unrelated or distantly related languages can be syntactically similar or dissimilar, but all closely related languages are syntactically sim￾ilar. Right: Relationship between character overlap (between …
Figure 5
Figure 5. Figure 5: Differences between the performance on the original test treebanks and their transliterated counterparts for POS tagging (top) and parsing (LAS, bottom). Rows are for test sets, columns for training sets. Pink cells mark configurations where scores are better on the or…
Figure 6
Figure 6. Figure 6: Differences between the n-gram-based topic classification performance on the original test languages and their transliterated counterparts. Rows are for test sets, columns for training sets. Pink cells mark configurations where scores are better on the original data; g…
Figure 7
Figure 7. Figure 7: POS tagging accuracy scores for all combinations of source (columns) and target languages (rows), ordered by target language clusters (Ward’s method). The darker a cell, the better the score. 22 [PITH_FULL_IMAGE:figures/full_fig_p022_7.png]
Figure 8
Figure 8. Figure 8: Labelled attachment scores for all combinations of source (columns) and target languages (rows), ordered by target language clusters (Ward’s method). The darker a cell, the better the score. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_8.png]
Figure 9
Figure 9. Figure 9: Unlabelled attachment scores for all combinations of source (columns) and target languages (rows), ordered by target language clusters (Ward’s method). The darker a cell, the better the score. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_9.png]
Figure 10
Figure 10. Figure 10: Topic classification accuracy scores (MLP with n-grams, original writing systems) for all combina￾tions of source (columns) and target languages (rows), ordered by target language clusters (Ward’s method). The darker a cell, the better the score. 25 [PITH_FULL_IMAGE:…
Figure 11
Figure 11. Figure 11: Topic classification accuracy scores (MLP with n-grams, transliterated data) for all combinations of source (columns) and target languages (rows), ordered by target language clusters (Ward’s method). The darker a cell, the better the score. 26 [PITH_FULL_IMAGE:figure…
Figure 12
Figure 12. Figure 12: Topic classification accuracy scores (MLP with mBERT representations) for all combinations of source (columns) and target languages (rows), ordered by target language clusters (Ward’s method). The darker a cell, the better the score. 27 [PITH_FULL_IMAGE:figures/full_…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

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

  1. CultureMERT: Continual Pre-Training for Cross-Cultural Music Representation Learning

    cs.SD 2025-06 conditional novelty 5.0 of 10

    CultureMERT adapts the MERT-95M music model to Greek, Turkish and Indian traditions via two-stage continual pre-training, improving non-Western auto-tagging by 4.9% on average while keeping Western performance intact.

  2. LAGO: Few-shot Crosslingual Embedding Inversion Attacks via Language Similarity-Aware Graph Optimization

    cs.CL 2025-05 conditional novelty 5.0 of 10

    LAGO shows that constraining alignment matrices of linguistically similar languages to be close improves few-shot cross-lingual embedding inversion accuracy over independent per-language baselines.

Reference graph

Works this paper leans on

62 extracted references · 29 canonical work pages · cited by 2 Pith papers

  1. [1]

    online" 'onlinestring :=

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRING...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

  3. [3]

    Alabi, Yanke Mao, Haonan Gao, and En-Shiun Annie Lee

    David Ifeoluwa Adelani, Hannah Liu, Xiaoyu Shen, Nikita Vassilyev, Jesujoba O. Alabi, Yanke Mao, Haonan Gao, and En-Shiun Annie Lee. 2024. https://aclanthology.org/2024.eacl-long.14/ SIB -200: A simple, inclusive, and big evaluation dataset for topic classification in 200+ languages and dialects . In Proceedings of the 18th Conference of the European Chap...

    2024

  4. [4]

    Alabi, Shamsuddeen H

    David Ifeoluwa Adelani, Graham Neubig, Sebastian Ruder, Shruti Rijhwani, Michael Beukman, Chester Palen-Michel, Constantine Lignos, Jesujoba O. Alabi, Shamsuddeen H. Muhammad, Peter Nabende, Cheikh M. Bamba Dione, Andiswa Bukula, Rooweither Mabuya, Bonaventure F. P. Dossou, Blessing Sibanda, Happy Buzaaba, Jonathan Mukiibi, Godson Kalipe, Derguene Mbaye, ...

    doi:10.18653/v1/2022.emnlp-main.298 2022

  5. [5]

    Kabir Ahuja, Shanu Kumar, Sandipan Dandapat, and Monojit Choudhury. 2022. https://doi.org/10.18653/v1/2022.acl-long.374 Multi task learning for zero shot performance prediction of multilingual models . In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5454--5467, Dublin, Ireland. Asso...

    doi:10.18653/v1/2022.acl-long.374 2022

  6. [6]

    Chitale, Raghavan AK, Sumanth Doddapaneni, Varun Gumma, Aswanth Kumar, Janki Nawale, Anupama Sujatha, Ratish Puduppully, Vivek Raghavan, Pratyush Kumar, Mitesh M

    AI4Bharat, Jay Gala, Pranjal A. Chitale, Raghavan AK, Sumanth Doddapaneni, Varun Gumma, Aswanth Kumar, Janki Nawale, Anupama Sujatha, Ratish Puduppully, Vivek Raghavan, Pratyush Kumar, Mitesh M. Khapra, Raj Dabre, and Anoop Kunchukuttan. 2023. https://arxiv.org/abs/arXiv:2305.16307 IndicTrans2 : Towards high-quality and accessible machine translation mode...

    arXiv 2023

  7. [7]

    Hamidreza Amirzadeh, Sadegh Jafari, Anika Harju, and Rob van der Goot. 2025. https://aclanthology.org/2025.coling-main.435/ data2lang2vec: Data driven typological features completion . In Proceedings of the 31st International Conference on Computational Linguistics, pages 6520--6529, Abu Dhabi, UAE. Association for Computational Linguistics

    work page 2025

  8. [8]

    Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2020. https://doi.org/10.18653/v1/2020.emnlp-main.618 Translation artifacts in cross-lingual transfer learning . In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7674--7684, Online. Association for Computational Linguistics

    doi:10.18653/v1/2020.emnlp-main.618 2020

Show all 62 references
  1. [9]

    Sina Bagheri Nezhad and Ameeta Agrawal. 2024. https://doi.org/10.18653/v1/2024.vardial-1.2 What drives performance in multilingual language models? In Proceedings of the Eleventh Workshop on NLP for Similar Languages, Varieties, and Dialects (VarDial 2024), pages 16--27, Mexic...

    2024 doi

  2. [10]

    Douglas Bates, Martin M \"a chler, Ben Bolker, and Steve Walker. 2015. https://doi.org/10.18637/jss.v067.i01 Fitting linear mixed-effects models using lme4 . Journal of Statistical Software, 67(1):1--48

    2015 doi

  3. [11]

    Emi Baylor, Esther Ploeger, and Johannes Bjerva. 2023. https://doi.org/10.18653/v1/2023.findings-emnlp.82 The past, present, and future of typological databases in NLP . In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 1163--1169, Singapore. Asso...

    2023 doi

  4. [12]

    Johannes Bjerva. 2024. https://doi.org/10.1162/coli_a_00498 The role of typological feature prediction in NLP and linguistics . Computational Linguistics, 50(2):781--794

    2024 doi

  5. [13]

    Lars Borin. 2013. https://doi.org/doi:10.1515/9783110305258.3 The why and how of measuring linguistic differences . In Lars Borin and Anju Saxena, editors, Approaches to Measuring Linguistic Differences, pages 3--26. De Gruyter Mouton, Berlin, Boston

    2013 doi

  6. [14]

    Choi, Joel Tetreault, and Amanda Stent

    Jinho D. Choi, Joel Tetreault, and Amanda Stent. 2015. https://doi.org/10.3115/v1/P15-1038 It depends: Dependency parser comparison using a web-based evaluation tool . In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th Intern...

    2015 doi

  7. [15]

    Chris Collins and Richard Kayne. 2011. Syntactic structures of the world's languages. New York University, New York

    2011

  8. [16]

    Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzm \'a n, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020. https://doi.org/10.18653/v1/2020.acl-main.747 Unsupervised cross-lingual representation learning ...

    2020 doi

  9. [17]

    Manning, Joakim Nivre, and Daniel Zeman

    Marie-Catherine de Marneffe, Christopher D. Manning, Joakim Nivre, and Daniel Zeman. 2021. https://doi.org/10.1162/coli_a_00402 U niversal D ependencies . Computational Linguistics, 47(2):255--308

    2021 doi

  10. [18]

    Wietse de Vries, Martijn Wieling, and Malvina Nissim. 2022. https://doi.org/10.18653/v1/2022.acl-long.529 Make the best of cross-lingual transfer: Evidence from POS tagging with over 100 languages . In Proceedings of the 60th Annual Meeting of the Association for Computational...

    2022 doi

  11. [19]

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. https://doi.org/10.18653/v1/N19-1423 BERT : Pre-training of deep bidirectional transformers for language understanding . In Proceedings of the 2019 Conference of the North A merican Chapter of the Associat...

    2019 doi

  12. [20]

    Condé, Kalo Mory Diané, Chris Piech, and Christopher Manning

    Moussa Doumbouya, Baba Mamadi Diané, Solo Farabado Cissé, Djibrila Diané, Abdoulaye Sow, Séré Moussa Doumbouya, Daouda Bangoura, Fodé Moriba Bayo, Ibrahima Sory 2. Condé, Kalo Mory Diané, Chris Piech, and Christopher Manning. 2023. https://aclanthology.org/2023.wmt-1.34 Machin...

    2023

  13. [21]

    Dryer and Martin Haspelmath, editors

    Matthew S. Dryer and Martin Haspelmath, editors. 2013. The World Atlas of Language Structures Online. Max Planck Institute for Evolutionary Anthropology

    2013

  14. [22]

    J. C. Gower. 1971. http://www.jstor.org/stable/2528823 A general coefficient of similarity and some of its properties . Biometrics, 27(4):857--871

    1971

  15. [23]

    Naman Goyal, Cynthia Gao, Vishrav Chaudhary, Peng-Jen Chen, Guillaume Wenzek, Da Ju, Sanjana Krishnan, Marc ' Aurelio Ranzato, Francisco Guzm \'a n, and Angela Fan. 2022. https://doi.org/10.1162/tacl_a_00474 The F lores-101 evaluation benchmark for low-resource and multilingua...

    2022 doi

  16. [24]

    Francisco Guzmán, Peng-Jen Chen, Myle Ott, Juan Pino, Guillaume Lample, Philipp Koehn, Vishrav Chaudhary, and Marc’Aurelio Ranzato. 2019. https://aclanthology.org/D19-1632 The FLORES evaluation datasets for low-resource machine translation: N epali -- E nglish and S inhala -- ...

    2019

  17. [25]

    Harald Hammarstr\" o m, Robert Forkel, Martin Haspelmath, and Sebastian Bank. 2024. Glottolog 5.0. Max Planck Institute for Evolutionary Anthropology, Leipzig. Available online at https://glottolog.org

    2024

  18. [26]

    Ulf Hermjakob, Jonathan May, and Kevin Knight. 2018. https://doi.org/10.18653/v1/P18-4003 Out-of-the-box universal R omanization tool uroman . In Proceedings of ACL 2018, System Demonstrations , pages 13--18, Melbourne, Australia. Association for Computational Linguistics

    2018 doi

  19. [27]

    Gerhard J\" a ger. 2018 a . https://doi.org/10.17605/OSF.IO/CUFV7 Extracting language distances and character matrices from ASJP data . OSF

    2018 doi

  20. [28]

    Gerhard J\" a ger. 2018 b . https://doi.org/10.1038/sdata.2018.189 Global-scale phylogenetic linguistic inference from lexical resources . Scientific data, 5(180189)

    2018 doi

  21. [29]

    Amir Hossein Kargaran, Fran c ois Yvon, and Hinrich Sch \"u tze. 2024. https://aclanthology.org/2024.lrec-main.687/ G lot S cript: A resource and tool for low resource writing system identification . In Proceedings of the 2024 Joint International Conference on Computational Li...

    2024

  22. [30]

    Hoang, Eric Khiu, A

    Aditya Khan, Mason Shipton, David Anugraha, Kaiyao Duan, Phuong H. Hoang, Eric Khiu, A. Seza Do g ru \"o z, and En-Shiun Annie Lee. 2025. https://aclanthology.org/2025.coling-main.463/ URIEL +: Enhancing linguistic inclusion and usability in a typological and multilingual know...

    2025

  23. [31]

    Anne Lauscher, Vinit Ravishankar, Ivan Vuli \'c , and Goran Glava s . 2020. https://doi.org/10.18653/v1/2020.emnlp-main.363 From zero to hero: O n the limitations of zero-shot language transfer with multilingual T ransformers . In Proceedings of the 2020 Conference on Empirica...

    2020 doi

  24. [32]

    Paul Lewis, Gary F

    M. Paul Lewis, Gary F. Simons, and Charles D. Fennig. 2015. Ethnologue: Languages of the world, eighteenth edition. SIL International, Dallas, Texas

    2015

  25. [33]

    Peiqin Lin, Chengzhi Hu, Zheyu Zhang, Andre Martins, and Hinrich Schuetze. 2024. https://aclanthology.org/2024.findings-eacl.20/ m PLM -sim: Better cross-lingual similarity and transfer in multilingual pretrained language models . In Findings of the Association for Computation...

    2024

  26. [34]

    Yu-Hsiang Lin, Chian-Yu Chen, Jean Lee, Zirui Li, Yuyan Zhang, Mengzhou Xia, Shruti Rijhwani, Junxian He, Zhisong Zhang, Xuezhe Ma, Antonios Anastasopoulos, Patrick Littell, and Graham Neubig. 2019. https://doi.org/10.18653/v1/P19-1301 Choosing transfer languages for cross-lin...

    2019 doi

  27. [35]

    Patrick Littell, David Mortensen, and Antonis Anastasopoulos. 2019. https://github.com/antonisa/lang2vec lang2vec 1.1.6

    2019

  28. [36]

    Mortensen, Ke Lin, Katherine Kairis, Carlisle Turner, and Lori Levin

    Patrick Littell, David R. Mortensen, Ke Lin, Katherine Kairis, Carlisle Turner, and Lori Levin. 2017. https://aclanthology.org/E17-2002/ URIEL and lang2vec: Representing languages as typological, geographical, and phylogenetic vectors . In Proceedings of the 15th Conference of...

    2017

  29. [37]

    Dan Malkin, Tomasz Limisiewicz, and Gabriel Stanovsky. 2022. https://doi.org/10.18653/v1/2022.naacl-main.361 A balanced data approach for evaluating cross-lingual transfer: Mapping the linguistic blood bank . In Proceedings of the 2022 Conference of the North American Chapter ...

    2022 doi

  30. [38]

    Ryan McDonald and Joakim Nivre. 2011. https://doi.org/10.1162/coli_a_00039 Analyzing and integrating dependency parsers . Computational Linguistics, 37(1):197--230

    2011 doi

  31. [39]

    Steven Moran, Daniel McCloy, and Richard Wright. 2014. PHOIBLE online. Max Planck Institute for Evolutionary Anthropology, Leipzig

    2014

  32. [40]

    Benjamin Muller, Deepanshu Gupta, Jean-Philippe Fauconnier, Siddharth Patwardhan, David Vandyke, and Sachin Agarwal. 2023. https://proceedings.mlr.press/v203/muller23a.html Languages you know influence those you learn: Impact of language characteristics on multi-lingual text-t...

    2023

  33. [41]

    Joakim Nivre and Chiao-Ting Fang. 2017. https://aclanthology.org/W17-0411/ U niversal D ependency evaluation . In Proceedings of the N o D a L i D a 2017 Workshop on Universal Dependencies ( UDW 2017) , pages 86--95, Gothenburg, Sweden. Association for Computational Linguistics

    2017

  34. [42]

    NLLB Team , Marta R. Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula, Loic Barrault, Gabriel Mejia-Gonzalez, Pran...

    2022 arXiv

  35. [43]

    Zhang, Coleman Haley, Kenneth Steimel, Han Liu, and Lane Schwartz

    Hyunji Hayley Park, Katherine J. Zhang, Coleman Haley, Kenneth Steimel, Han Liu, and Lane Schwartz. 2021. https://doi.org/10.1162/tacl_a_00365 Morphology matters: A multilingual language modeling analysis . Transactions of the Association for Computational Linguistics, 9:261--276

    2021 doi

  36. [44]

    Pedregosa, G

    F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine learning in P ython. Journal of Machine L...

    2011

  37. [45]

    Fred Philippy, Siwen Guo, and Shohreh Haddadan. 2023. https://doi.org/10.18653/v1/2023.acl-long.323 Towards a common understanding of contributing factors for cross-lingual transfer in multilingual language models: A review . In Proceedings of the 61st Annual Meeting of the As...

    2023 doi

  38. [46]

    Telmo Pires, Eva Schlinger, and Dan Garrette. 2019. https://doi.org/10.18653/v1/P19-1493 How multilingual is multilingual BERT ? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4996--5001, Florence, Italy. Association for Compu...

    2019 doi

  39. [47]

    Vitaly Protasov, Elisei Stakovskii, Ekaterina Voloshina, Tatiana Shavrina, and Alexander Panchenko. 2024. https://doi.org/10.18653/v1/2024.loresmt-1.10 Super donors and super recipients: Studying cross-lingual transfer between high-resource and low-resource languages . In Proc...

    2024 doi

  40. [48]

    R Core Team . 2024. https://www.R-project.org/ R: A language and environment for statistical computing

    2024

  41. [49]

    Enora Rice, Ali Marashian, Hannah Haynie, Katharina von der Wense, and Alexis Palmer. 2025. https://arxiv.org/abs/2503.19979 Untangling the influence of typology, data and model architecture on ranking transfer languages for cross-lingual POS tagging . Preprint, arXiv:2503.19979

    2025 arXiv

  42. [50]

    Tanja Samard z i \'c , Ximena Gutierrez-Vasques, Rob van der Goot, Max M \"u ller-Eberstein, Olga Pelloni, and Barbara Plank. 2022. https://doi.org/10.18653/v1/2022.conll-1.18 On language spaces, scales and cross-lingual transfer of UD parsers . In Proceedings of the 26th Conf...

    2022 doi

  43. [51]

    Haynie, Dami \'a n E

    Hedvig Skirg rd, Hannah J. Haynie, Dami \'a n E. Blasi, Harald Hammarstr \"o m, Jeremy Collins, Jay J. Latarche, Jakob Lesage, Tobias Weber, Alena Witzlack-Makarevich, Sam Passmore, Angela Chira, Luke Maurits, Russell Dinnage, Michael Dunn, Ger Reesink, Ruth Singer, Claire Bow...

    2023 doi

  44. [52]

    Richard Sproat and Alexander Gutkin. 2021. https://doi.org/10.1162/coli_a_00409 The taxonomy of writing systems: How to measure how logographic a system is . Computational Linguistics, 47(3):477--528

    2021 doi

  45. [53]

    Anirudh Srinivasan, Sunayana Sitaram, Tanuja Ganu, Sandipan Dandapat, Kalika Bali, and Monojit Choudhury. 2021. https://arxiv.org/abs/2110.08875 Predicting the performance of multilingual NLP models . Computing Research Repository, arXiv:2110.08875

    2021 arXiv

  46. [54]

    Milan Straka. 2018. https://doi.org/10.18653/v1/K18-2020 UDP ipe 2.0 prototype at C o NLL 2018 UD shared task . In Proceedings of the C o NLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies , pages 197--207, Brussels, Belgium. Association for Com...

    2018 doi

  47. [55]

    Milan Straka. 2023. http://hdl.handle.net/11234/1-5200 Universal Dependencies 2.12 models for UDPipe 2 (2023-07-17) . LINDAT / CLARIAH - CZ digital library at the Institute of Formal and Applied Linguistics ( \'U FAL ), Faculty of Mathematics and Physics, Charles University

    2023

  48. [56]

    Seza Do g ru \"o z, and En-Shiun Lee

    Hasti Toossi, Guo Huai, Jinyu Liu, Eric Khiu, A. Seza Do g ru \"o z, and En-Shiun Lee. 2024. https://doi.org/10.18653/v1/2024.naacl-srw.25 A reproducibility study on quantifying language similarity: The impact of missing values in the URIEL knowledge base . In Proceedings of t...

    2024 doi

  49. [57]

    Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, St \'e fan J

    Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, St \'e fan J. van der Walt , Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nels...

    2020 doi

  50. [58]

    Ward, Jr

    Joe H. Ward, Jr. 1963. https://doi.org/10.1080/01621459.1963.10500845 Hierarchical grouping to optimize an objective function . Journal of the American Statistical Association, 58(301):236--244

    1963

  51. [59]

    White and Ryan Cotterell

    Jennifer C. White and Ryan Cotterell. 2021. https://doi.org/10.18653/v1/2021.acl-long.38 Examining the inductive bias of neural language models with artificial languages . In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th I...

    2021 doi

  52. [60]

    Holman, and Cecil H

    Søren Wichmann, Eric W. Holman, and Cecil H. Brown. 2016. https://asjp.clld.org/ The ASJP database (version 17) . Zenodo

    2016

  53. [61]

    Mengzhou Xia, Antonios Anastasopoulos, Ruochen Xu, Yiming Yang, and Graham Neubig. 2020. https://doi.org/10.18653/v1/2020.acl-main.764 Predicting performance for natural language processing tasks . In Proceedings of the 58th Annual Meeting of the Association for Computational ...

    2020 doi

  54. [62]

    Daniel Zeman, Joakim Nivre, Mitchell Abrams, Elia Ackermann, No \"e mi Aepli, Hamid Aghaei, Z eljko Agi \'c , Amir Ahmadi, Lars Ahrenberg, Chika Kennedy Ajede, Salih Furkan Akkurt, Gabriel \.e Aleksandravi c i \=u t \.e , Ika Alfina, Avner Algom, Khalid Alnajjar, Chiara Alzett...

    2024

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.