Local Structure Matters Most in Most Languages

Louis Clouatre, Prasanna Parthasarathi, Amal Zouaq, Sarath Chandar

Abstract
Many recent perturbation studies have found unintuitive results on what does and does not matter when performing Natural Language Understanding (NLU) tasks in English. Coding properties, such as the order of words, can often be removed through shuffling without impacting downstream performances. Such insight may be used to direct future research into English NLP models. As many improvements in multilingual settings consist of wholesale adaptation of English approaches, it is important to verify whether those studies replicate or not in multilingual settings. In this work, we replicate a study on the importance of local structure, and the relative unimportance of global structure, in a multilingual setting. We find that the phenomenon observed on the English language broadly translates to over 120 languages, with a few caveats.
Anthology ID:
2022.aacl-short.35
Volume:
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
Month:
November
Year:
2022
Address:
Online only
Editors:
Yulan He, Heng Ji, Sujian Li, Yang Liu, Chua-Hui Chang
Venues:
AACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
285–294
Language:
URL:
https://aclanthology.org/2022.aacl-short.35
DOI:
Bibkey:
Cite (ACL):
Louis Clouatre, Prasanna Parthasarathi, Amal Zouaq, and Sarath Chandar. 2022. Local Structure Matters Most in Most Languages. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 285–294, Online only. Association for Computational Linguistics.
Cite (Informal):
Local Structure Matters Most in Most Languages (Clouatre et al., AACL-IJCNLP 2022)
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