Factual Consistency of Multilingual Pretrained Language Models
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Pretrained language models can be queried for factual knowledge, with potential applications in knowledge base acquisition and tasks that require inference. However, for that, we need to know how reliable this knowledge is, and recent work has shown that monolingual English language models lack consistency when predicting factual knowledge, that is, they fill-in-the-blank differently for paraphrases describing the same fact. In this paper, we extend the analysis of consistency to a multilingual setting. We introduce a resource, MPARAREL, and investigate (i) whether multilingual language models such as mBERT and XLM-R are more consistent than their monolingual counterparts; and (ii) if such models are equally consistent across languages. We find that mBERT is as inconsistent as English BERT in English paraphrases, but that both mBERT and XLM-R exhibit a high degree of inconsistency in English and even more so for all the other 45 languages.
Original language | English |
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Title of host publication | ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, Findings of ACL 2022 |
Editors | Smaranda Muresan, Preslav Nakov, Aline Villavicencio |
Publisher | Association for Computational Linguistics (ACL) |
Publication date | 2022 |
Pages | 3046-3052 |
ISBN (Electronic) | 9781955917254 |
Publication status | Published - 2022 |
Event | 60th Annual Meeting of the Association for Computational Linguistics, ACL 2022 - Dublin, Ireland Duration: 22 May 2022 → 27 May 2022 |
Conference
Conference | 60th Annual Meeting of the Association for Computational Linguistics, ACL 2022 |
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Land | Ireland |
By | Dublin |
Periode | 22/05/2022 → 27/05/2022 |
Sponsor | Amazon Science, Bloomberg Engineering, et al., Google Research, Liveperson, Meta |
Bibliographical note
Publisher Copyright:
© 2022 Association for Computational Linguistics.
ID: 341485866