# Multilingual Word Sense Disambiguation with Unified Sense Representation

arXiv: https://arxiv.org/abs/2210.07447 · HTML: https://mlsanity.com/paper/2210.07447/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 42 references that are in the map.
- First posted on arXiv 14 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Word Alignment by Fine-tuning Embeddings on Parallel Corpora](https://mlsanity.com/paper/2101.08231/) (2021)
- [GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge](https://mlsanity.com/paper/1908.07245/) (2019)
- [Moving Down the Long Tail of Word Sense Disambiguation with Gloss-Informed Biencoders](https://mlsanity.com/paper/2005.02590/) (2020)
- [Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation](https://mlsanity.com/paper/1906.10007/) (2019)
- [Incorporating Glosses into Neural Word Sense Disambiguation](https://mlsanity.com/paper/1805.08028/) (2018)
- [Handling Homographs in Neural Machine Translation](https://mlsanity.com/paper/1708.06510/) (2017)
- [Integrating Weakly Supervised Word Sense Disambiguation into Neural Machine Translation](https://mlsanity.com/paper/1810.02614/) (2018)
- [Semi-Supervised and Unsupervised Sense Annotation via Translations](https://mlsanity.com/paper/2106.06462/) (2021)

## Built on by (most cited first, 5 of 5)

- [Efficient Zero-shot Event Extraction with Context-Definition Alignment](https://mlsanity.com/paper/2211.05156/) (2022)
- [A Survey on Lexical Ambiguity Detection and Word Sense Disambiguation](https://mlsanity.com/paper/2403.16129/) (2024)
- [Thank You, Stingray: Multilingual Large Language Models Can Not (Yet) Disambiguate Cross-Lingual Word Sense](https://mlsanity.com/paper/2410.21573/) (2024)
- [Translate to Disambiguate: Zero-shot Multilingual Word Sense Disambiguation with Pretrained Language Models](https://mlsanity.com/paper/2304.13803/) (2023)
- [Word Sense Disambiguation in Native Spanish: A Comprehensive Lexical Evaluation Resource](https://mlsanity.com/paper/2409.20524/) (2024)

## Abstract (arXiv)

As a key natural language processing (NLP) task, word sense disambiguation (WSD) evaluates how well NLP models can understand the lexical semantics of words under specific contexts. Benefited from the large-scale annotation, current WSD systems have achieved impressive performances in English by combining supervised learning with lexical knowledge. However, such success is hard to be replicated in other languages, where we only have limited annotations.In this paper, based on the multilingual lexicon BabelNet describing the same set of concepts across languages, we propose building knowledge and supervised-based Multilingual Word Sense Disambiguation (MWSD) systems. We build unified sense representations for multiple languages and address the annotation scarcity problem for MWSD by transferring annotations from rich-sourced languages to poorer ones. With the unified sense representations, annotations from multiple languages can be jointly trained to benefit the MWSD tasks. Evaluations of SemEval-13 and SemEval-15 datasets demonstrate the effectiveness of our methodology.