Adapting BERT for Word Sense Disambiguation with Gloss Selection Objective and Example Sentences (2020.findings-emnlp)
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| Challenge: | Existing studies have used pre-trained language models for domain adaptation or transfer learning to improve natural language processing performance. |
| Approach: | They propose to fine-tune word sense disambiguation on sequence-pair ranking task and to use existing WordNet examples to augment the model. |
| Outcome: | The proposed model achieves state-of-the-art on the English all-words benchmark datasets. |
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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. |
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. |
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. |
LTRS: Improving Word Sense Disambiguation via Learning to Rank Senses (2025.coling-main)
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| Challenge: | Conventional training strategies only consider predefined senses for target words and learn each of them from relatively limited instances, neglecting the influence of similar ones. |
| Approach: | They propose a method to rank senses to improve the task of word Sense Disambiguation (WSD) by ranking an expanded list of sense definitions. |
| Outcome: | The proposed method achieves a SOTA F1 score of 79.6% in Chinese WSD and shows faster convergence than previous methods. |
Leveraging Gloss Knowledge in Neural Word Sense Disambiguation by Hierarchical Co-Attention (D18-1)
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| Challenge: | Existing models for Word Sense Disambiguation use labeled data, but lack gloss knowledge. |
| Approach: | They propose a co-attention mechanism to generate co-dependent representations for context and gloss . they propose to incorporate gloss knowledge into neural networks for Word Sense Disambiguation . |
| Outcome: | The proposed model achieves state-of-the-art results on standard English all-words WSD datasets. |
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 . |
Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources (L18-1)
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| Challenge: | Existing supervised models for Word Sense Disambiguation (WSD) are limited to knowledge-based approaches. |
| Approach: | They propose to use WordNet and WordNet Domains to enhance supervised WSD models by introducing semantic features into the classifiers and using the SLR structure to augment training data. |
| Outcome: | The proposed model improves the state-of-the-art in Word Sense Disambiguation (WSD) The proposed approach is compared with the state of the art in the most popular benchmarks. |
Semantic Specialization for Knowledge-based Word Sense Disambiguation (2023.eacl-main)
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| Challenge: | Existing methods for knowledge-based Word Sense Disambiguation (WSD) use only lexical knowledge to adapt contextualized embeddings. |
| Approach: | They propose a semantic specialization where contextualized embeddings are adapted to the WSD task using only lexical knowledge. |
| Outcome: | The proposed method outperforms previous studies that adapt contextualized embeddings while controlling deviations from the original embeddables. |
Word Sense Disambiguation for 158 Languages using Word Embeddings Only (2020.lrec-1)
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Varvara Logacheva, Denis Teslenko, Artem Shelmanov, Steffen Remus, Dmitry Ustalov, Andrey Kutuzov, Ekaterina Artemova, Chris Biemann, Simone Paolo Ponzetto, Alexander Panchenko
| Challenge: | Existing methods of disambiguation of word senses are based on knowledge bases, taxonomies, and other externally built resources. |
| Approach: | They propose a method that takes a pre-trained word embedding model and induces a fully-fledged word sense inventory for 158 languages. |
| Outcome: | The proposed model is based on a pre-trained word embedding model and induces a fully-fledged word sense inventory in 158 languages. |
Incorporating Glosses into Neural Word Sense Disambiguation (P18-1)
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| Challenge: | Existing neural networks for Word Sense Disambiguation rely on labeled data and lexical knowledge. |
| Approach: | They propose a gloss-augmented WSD neural network which integrates context and glosses of the target word into a unified framework. |
| Outcome: | The proposed model outperforms the state-of-the-art systems on several English all-words WSD datasets. |