翻译以消歧：基于预训练语言模型的零样本多语言词义消歧
Translate to Disambiguate: Zero-shot Multilingual Word Sense Disambiguation with Pretrained Language Models
AI总结：
本研究针对预训练语言模型零样本词义消歧效果不佳的问题，提出基于上下文词级翻译的零样本WSD方法，在18种语言上无需微调即超越全监督基线，为利用PLM跨语言知识实现鲁棒零样本推理提供了思路。
摘要：
预训练语言模型（PLMs）可学习丰富的跨语言知识，经微调后能在翻译、多语言词义消歧（WSD）等各类任务上取得良好表现。但在零样本场景下，它们往往难以完成词义消歧。为厘清这一差异，我们开展了一项新研究，探究PLM通过上下文词级翻译（C-WLT）——一种提示模型翻译上下文中给定词的词级翻译扩展方法——对跨语言词义的捕捉能力。研究发现，随着模型规模增大，PLM会编码更多跨语言词义知识，且能更好地利用上下文提升WLT性能。基于C-WLT，我们提出了一种WSD零样本方法，在XL-WSD数据集的18种语言上进行了测试。该方法无需额外训练或微调，在多数评估语言的召回率上优于全监督基线。本研究为理解如何最优利用PLM内部的跨语言知识，实现任意语言下鲁棒的零样本推理迈出了第一步。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
