| Challenge: | Word Sense Disambiguation (WSD) is a key task in Natural Language Processing (NLP) however, how well these models inherently disambiguate word senses remains uncertain. |
| Approach: | They evaluate several encoder-only PLMs across WordNet and ODE sense inventories to evaluate their ability to separate word senses without any task-specific fine-tuning. |
| Outcome: | The proposed model outperforms output layer on WordNet and ODE sense inventories by 15 percentage points. |
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Lorenzo Proietti, Stefano Perrella, Simone Tedeschi, Giulia Vulpis, Leonardo Lavalle, Andrea Sanchietti, Andrea Ferrari, Roberto Navigli
| Challenge: | Word Sense Disambiguation (WSD) is a key task in Natural Language Processing (NLP) but current pretrained language models lack the granularity to perform disambiguation . |
| Approach: | They propose a large-scale resource that leverages homonymy relations to cluster WordNet senses and train Homonymy Disambiguation systems. |
| Outcome: | The proposed model can distinguish homonyms with up to 95% accuracy even without fine-tuning the underlying PLM. |
Improved Word Sense Disambiguation with Enhanced Sense Representations (2021.findings-emnlp)
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| Challenge: | Existing supervised word sense disambiguation systems do not provide enough information about word senses. |
| Approach: | They propose to incorporate synonyms, example phrases or sentences showing usage of word senses and sense gloss of hypernyms into the sense representations. |
| Outcome: | The proposed system achieves an F1 score of 82.0% on the standard benchmark test dataset of the English all-words WSD task. |
Word Sense Linking: Disambiguating Outside the Sandbox (2024.findings-acl)
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| Challenge: | Word Sense Disambiguation (WSD) systems have performed well on several evaluation benchmarks, but it still struggles to find downstream applications. |
| Approach: | They propose a task where systems have to identify which spans to disambiguate and link them to their most suitable meaning. |
| Outcome: | The proposed task performs above the estimated inter-annotator agreement on a set of words . the proposed system is based on 'transformer-based' architectures and iteratively relaxes the assumptions . |
Do Large Language Models Understand Word Senses? (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have set new performance standards in a wide range of tasks. |
| Approach: | They evaluate the Word Sense Disambiguation capabilities of instruction-tuned LLMs and their ability to understand word senses in three generative settings: definition generation, free-form explanation, and example generation. |
| Outcome: | The proposed models can explain the meaning of words in context with 98% accuracy, while demonstrating greater robustness across domains and levels of difficulty. |
Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representations (D19-1)
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| Challenge: | Contextualized word representations are effective in downstream tasks such as question answering, named entity recognition, and sentiment analysis. |
| Approach: | They propose to integrate pre-trained contextualized word representations into a neural network that captures the whole sentence and the word representation in the sentence. |
| Outcome: | The proposed approach outperforms the state-of-the-art approach that makes use of non-contextualized word embeddings on multiple benchmark WSD datasets. |
Nibbling at the Hard Core of Word Sense Disambiguation (2022.acl-long)
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| Challenge: | Word Sense Disambiguation (WSD) is a task that is based on a set of pre-trained language models. |
| Approach: | They propose to use Word Sense Disambiguation to test whether systems can handle ambiguous words. |
| Outcome: | The proposed benchmarks show that seven of the most representative state-of-the-art systems make trivial errors on traditional evaluation benchmarks. |
A Deep Dive into Word Sense Disambiguation with LSTM (C18-1)
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| Challenge: | LSTM-based language models have been shown effective in Word Sense Disambiguation (WSD) but neither the training data nor the source code was released. |
| Approach: | They propose to use LSTM-based language models to perform Word Sense Disambiguation (WSD) using openly available datasets and software. |
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Moving Down the Long Tail of Word Sense Disambiguation with Gloss Informed Bi-encoders (2020.acl-main)
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| Challenge: | Existing models for Word Sense Disambiguation are not uniformly distributed on rare or unseen senses. |
| Approach: | They propose a bi-encoder model that embeds the target word with its context and the dictionary definition, or gloss, of each sense. |
| Outcome: | The proposed model outperforms previous state-of-the-art models on English all-words WSD, with a 31.1% error reduction on less frequent senses over prior work. |
GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge (D19-1)
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| Challenge: | Word Sense Disambiguation (WSD) aims to find the exact sense of an ambiguous word in a particular context. |
| Approach: | They propose to integrate gloss knowledge into supervised neural networks for Word Sense Disambiguation (WSD) this paper proposes to fine-tune a pre-trained BERT model and achieve new state-of-the-art results on WSD task. |
| Outcome: | The proposed model achieves state-of-the-art on the word Sense Disambiguation (WSD) task. |
Sense-Annotated Corpora for Word Sense Disambiguation in Multiple Languages and Domains (2020.lrec-1)
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| Challenge: | Word Sense Disambiguation (WSD) is a field of NLP where data is usually tied to a specific language. |
| Approach: | They propose to release five large datasets annotated with word-senses in five different languages and 5 datasets in English for a different semantic domain. |
| Outcome: | The study shows that supervised models trained on the data achieve higher performance than those trained on other corpora. |