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        <title>Translate to Disambiguate: Zero-shot Multilingual Word Sense Disambiguation with Pretrained Language Models</title>
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    <abstract>Pretrained Language Models (PLMs) learn rich cross-lingual knowledge and perform well on diverse tasks such as translation and multilingual word sense disambiguation (WSD) when finetuned. However, they often struggle at disambiguating word sense in a zero-shot setting. To better understand this contrast, we present a new study investigating how well PLMs capture cross-lingual word sense with Contextual Word-Level Translation (C-WLT), an extension of word-level translation that prompts the model to translate a given word in context. We find that as the model size increases, PLMs encode more cross-lingual word sense knowledge and better use context to improve WLT performance. Building on C-WLT, we introduce a zero-shot prompting approach for WSD, tested on 18 languages from the XL-WSD dataset. Our method outperforms fully supervised baselines on recall for many evaluation languages without additional training or finetuning. This study presents a first step towards understanding how to best leverage the cross-lingual knowledge inside PLMs for robust zero-shot reasoning in any language.</abstract>
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%0 Conference Proceedings
%T Translate to Disambiguate: Zero-shot Multilingual Word Sense Disambiguation with Pretrained Language Models
%A Kang, Haoqiang
%A Blevins, Terra
%A Zettlemoyer, Luke
%Y Graham, Yvette
%Y Purver, Matthew
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F kang-etal-2024-translate
%X Pretrained Language Models (PLMs) learn rich cross-lingual knowledge and perform well on diverse tasks such as translation and multilingual word sense disambiguation (WSD) when finetuned. However, they often struggle at disambiguating word sense in a zero-shot setting. To better understand this contrast, we present a new study investigating how well PLMs capture cross-lingual word sense with Contextual Word-Level Translation (C-WLT), an extension of word-level translation that prompts the model to translate a given word in context. We find that as the model size increases, PLMs encode more cross-lingual word sense knowledge and better use context to improve WLT performance. Building on C-WLT, we introduce a zero-shot prompting approach for WSD, tested on 18 languages from the XL-WSD dataset. Our method outperforms fully supervised baselines on recall for many evaluation languages without additional training or finetuning. This study presents a first step towards understanding how to best leverage the cross-lingual knowledge inside PLMs for robust zero-shot reasoning in any language.
%U https://aclanthology.org/2024.eacl-long.94
%P 1562-1575

Markdown (Informal)

[Translate to Disambiguate: Zero-shot Multilingual Word Sense Disambiguation with Pretrained Language Models](https://aclanthology.org/2024.eacl-long.94) (Kang et al., EACL 2024)

• Translate to Disambiguate: Zero-shot Multilingual Word Sense Disambiguation with Pretrained Language Models (Kang et al., EACL 2024)

ACL

• Haoqiang Kang, Terra Blevins, and Luke Zettlemoyer. 2024. Translate to Disambiguate: Zero-shot Multilingual Word Sense Disambiguation with Pretrained Language Models. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1562–1575, St. Julian’s, Malta. Association for Computational Linguistics.