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

arXiv: https://arxiv.org/abs/2110.02386 · HTML: https://mlsanity.com/paper/2110.02386/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 22 references that are in the map.
- First posted on arXiv 5 Oct 2021.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://mlsanity.com/paper/2005.04118/) (2020)
- [Adversarial NLI: A New Benchmark for Natural Language Understanding](https://mlsanity.com/paper/1910.14599/) (2019)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [On the Cross-lingual Transferability of Monolingual Representations](https://mlsanity.com/paper/1910.11856/) (2019)
- [Cross-Lingual Ability of Multilingual BERT: An Empirical Study](https://mlsanity.com/paper/1912.07840/) (2019)
- [Emerging Cross-lingual Structure in Pretrained Language Models](https://mlsanity.com/paper/1911.01464/) (2019)
- [Probing Natural Language Inference Models through Semantic Fragments](https://mlsanity.com/paper/1909.07521/) (2019)
- [TaxiNLI: Taking a Ride up the NLU Hill](https://mlsanity.com/paper/2009.14505/) (2020)

## Built on by (most cited first, 6 of 6)

- [IndicXNLI: Evaluating Multilingual Inference for Indian Languages](https://mlsanity.com/paper/2204.08776/) (2022)
- [Generalized Quantifiers as a Source of Error in Multilingual NLU Benchmarks](https://mlsanity.com/paper/2204.10615/) (2022)
- [Adapting BigScience Multilingual Model to Unseen Languages](https://mlsanity.com/paper/2204.04873/) (2022)
- [Empowering Cross-lingual Behavioral Testing of NLP Models with Typological Features](https://mlsanity.com/paper/2307.05454/) (2023)
- [Evaluating Inter-Bilingual Semantic Parsing for Indian Languages](https://mlsanity.com/paper/2304.13005/) (2023)
- [Investigating Lexical Sharing in Multilingual Machine Translation for Indian Languages](https://mlsanity.com/paper/2305.03207/) (2023)

## 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.