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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Analyzing the Effects of Reasoning Types on Cross-Lingual Transfer Performance
%A K, Karthikeyan
%A Sathe, Aalok
%A Aditya, Somak
%A Choudhury, Monojit
%Y Ataman, Duygu
%Y Birch, Alexandra
%Y Conneau, Alexis
%Y Firat, Orhan
%Y Ruder, Sebastian
%Y Sahin, Gozde Gul
%S Proceedings of the 1st Workshop on Multilingual Representation Learning
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F k-etal-2021-analyzing
%X 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.
%R 10.18653/v1/2021.mrl-1.8
%U https://aclanthology.org/2021.mrl-1.8
%U https://doi.org/10.18653/v1/2021.mrl-1.8
%P 86-95

Markdown (Informal)

[Analyzing the Effects of Reasoning Types on Cross-Lingual Transfer Performance](https://aclanthology.org/2021.mrl-1.8) (K et al., MRL 2021)

• Analyzing the Effects of Reasoning Types on Cross-Lingual Transfer Performance (K et al., MRL 2021)

ACL

• Karthikeyan K, Aalok Sathe, Somak Aditya, and Monojit Choudhury. 2021. Analyzing the Effects of Reasoning Types on Cross-Lingual Transfer Performance. In Proceedings of the 1st Workshop on Multilingual Representation Learning, pages 86–95, Punta Cana, Dominican Republic. Association for Computational Linguistics.