Translate to Disambiguate: Zero-shot Multilingual Word Sense Disambiguation with Pretrained Language Models
This work addresses the challenge of disambiguating word sense in multiple languages without task-specific training, which is incremental as it builds on existing PLM capabilities.
The paper tackled the problem of zero-shot multilingual word sense disambiguation (WSD) by investigating how pretrained language models (PLMs) capture cross-lingual word sense knowledge, and introduced a zero-shot approach that outperforms fully supervised baselines on recall for many languages in the XL-WSD dataset.
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.