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

**Authors:** Haoqiang Kang, Terra Blevins, Luke Zettlemoyer  
**arXiv:** [2304.13803](https://arxiv.org/abs/2304.13803)  
**Date:** 2023-04-26  
**Summary page:** https://www.summarizepaper.com/fr/paper/2304.13803v1/

## 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.