Translate to Disambiguate: Zero-shot Multilingual Word Sense Disambiguation with Pretrained Language Models

• URL: http://arxiv.org/abs/2304.13803v1
• Date: Wed, 26 Apr 2023 19:55:52 GMT
• Title: Translate to Disambiguate: Zero-shot Multilingual Word Sense Disambiguation with Pretrained Language Models
• Authors: Haoqiang Kang and Terra Blevins and Luke Zettlemoyer
• Abstract summary: Pretrained Language Models (PLMs) learn rich cross-lingual knowledge and can be finetuned to perform well on diverse tasks. We present a new study investigating how well PLMs capture cross-lingual word sense with Contextual Word-Level Translation (C-WLT) We find that as the model size increases, PLMs encode more cross-lingual word sense knowledge and better use context to improve WLT performance.
• Score: 67.19567060894563
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pretrained Language Models (PLMs) learn rich cross-lingual knowledge and can be finetuned to perform well on diverse tasks such as translation and multilingual word sense disambiguation (WSD). 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 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.

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