mlsanityData to 7 Oct 2026

Analyzing the Effects of Reasoning Types on Cross-Lingual Transfer Performance

arXiv 2110.02386 ↗First posted 5 Oct 2021cs.CL cs.AIcited by 6

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Abstract (arXiv)

Multilingual language models achieve impressive zero-shot accuracies in many languages in complex tasks such as Natural Language Inference (NLI). Examples in NLI (and equivalent complex tasks) often pertain to various types of sub-tasks, requiring different kinds of reasoning. Certain types of reasoning have proven to be more difficult to learn in a monolingual context, and in the crosslingual context, similar observations may shed light on zero-shot transfer efficiency and few-shot sample selection. Hence, to investigate the effects of types of reasoning on transfer performance, we propose a category-annotated multilingual NLI dataset and discuss the challenges to scale monolingual annotations to multiple languages. We statistically observe interesting effects that the confluence of reasoning types and language similarities have on transfer performance.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 15 of its 22 references that are in the map. First posted on arXiv 5 Oct 2021.

Builds on

15 of 22 references.

References · 15

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
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  7. 2020Beyond Accuracy: Behavioral Testing of NLP models with CheckListcited by 979
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  9. 2020The State and Fate of Linguistic Diversity and Inclusion in the NLP Worldcited by 789
  10. 2020XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalizationcited by 738
  11. 2019On the Cross-lingual Transferability of Monolingual Representationscited by 575
  12. 2019Cross-Lingual Ability of Multilingual BERT: An Empirical Studycited by 182
3 more
  1. 2019Emerging Cross-lingual Structure in Pretrained Language Modelscited by 180
  2. 2019Probing Natural Language Inference Models through Semantic Fragmentscited by 64
  3. 2020TaxiNLI: Taking a Ride up the NLU Hillcited by 27

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Cited by year

Citing papers per year: 2022: 3; 2023: 3.

6 papers.

  1. 2022IndicXNLI: Evaluating Multilingual Inference for Indian Languagescited by 26
  2. 2022Generalized Quantifiers as a Source of Error in Multilingual NLU Benchmarkscited by 8
  3. 2022Adapting BigScience Multilingual Model to Unseen Languagescited by 6
  4. 2023Empowering Cross-lingual Behavioral Testing of NLP Models with Typological Featurescited by 6
  5. 2023Evaluating Inter-Bilingual Semantic Parsing for Indian Languagescited by 1
  6. 2023Investigating Lexical Sharing in Multilingual Machine Translation for Indian Languagescited by 1