Factual Consistency of Multilingual Pretrained Language Models
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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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.
Originalsprog | Engelsk |
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Titel | ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, Findings of ACL 2022 |
Redaktører | Smaranda Muresan, Preslav Nakov, Aline Villavicencio |
Forlag | Association for Computational Linguistics (ACL) |
Publikationsdato | 2022 |
Sider | 3046-3052 |
ISBN (Elektronisk) | 9781955917254 |
Status | Udgivet - 2022 |
Begivenhed | 60th Annual Meeting of the Association for Computational Linguistics, ACL 2022 - Dublin, Irland Varighed: 22 maj 2022 → 27 maj 2022 |
Konference
Konference | 60th Annual Meeting of the Association for Computational Linguistics, ACL 2022 |
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Land | Irland |
By | Dublin |
Periode | 22/05/2022 → 27/05/2022 |
Sponsor | Amazon Science, Bloomberg Engineering, et al., Google Research, Liveperson, Meta |
Bibliografisk note
Publisher Copyright:
© 2022 Association for Computational Linguistics.
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