mlsanityData to 7 Oct 2026

Multilingual Word Sense Disambiguation with Unified Sense Representation

arXiv 2210.07447 ↗First posted 14 Oct 2022cs.CLcited by 5

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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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 11 of its 42 references that are in the map. First posted on arXiv 14 Oct 2022.

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11 of 42 references.

References · 11

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,471
  4. 2021Word Alignment by Fine-tuning Embeddings on Parallel Corporacited by 186
  5. 2019GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledgecited by 78
  6. 2020Moving Down the Long Tail of Word Sense Disambiguation with Gloss-Informed Biencoderscited by 61
  7. 2019Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguationcited by 40
  8. 2018Incorporating Glosses into Neural Word Sense Disambiguationcited by 18
  9. 2017Handling Homographs in Neural Machine Translationcited by 11
  10. 2018Integrating Weakly Supervised Word Sense Disambiguation into Neural Machine Translationcited by 7
  11. 2021Semi-Supervised and Unsupervised Sense Annotation via Translationscited by 2

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 3.

5 papers.

  1. 2022Efficient Zero-shot Event Extraction with Context-Definition Alignmentcited by 11
  2. 2024A Survey on Lexical Ambiguity Detection and Word Sense Disambiguationcited by 5
  3. 2024Thank You, Stingray: Multilingual Large Language Models Can Not (Yet) Disambiguate Cross-Lingual Word Sensecited by 4
  4. 2023Translate to Disambiguate: Zero-shot Multilingual Word Sense Disambiguation with Pretrained Language Modelscited by 2
  5. 2024Word Sense Disambiguation in Native Spanish: A Comprehensive Lexical Evaluation Resourcecited by 0