| 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. |
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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. |
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. |
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. |
Improving HowNet-Based Chinese Word Sense Disambiguation with Translations (2022.findings-emnlp)
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| Challenge: | Prior work on unsupervised WSD has leveraged lexical knowledge bases, such as WordNet and BabelNet, but these have proven to be less effective for Chinese. |
| Approach: | They propose a system which combines contextual information from a pretrained neural language model with bilingual information obtained via machine translation and sense translation information from HowNet. |
| Outcome: | The proposed system achieves a state-of-the-art for unsupervised Chinese WSD. |
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. |
Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories (2021.emnlp-main)
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| Challenge: | Existing supervised models struggle to make correct predictions on rare word senses due to limited training data. |
| Approach: | They propose a gloss alignment algorithm that can align definition sentences with the same meaning from different sense inventories to collect rich lexical knowledge. |
| Outcome: | The proposed method outperforms previous methods on both frequent and rare word senses. |
MWE as WSD: Solving Multiword Expression Identification with Word Sense Disambiguation (2023.findings-emnlp)
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| Challenge: | Recent approaches to word sense disambiguation use encodings of the sense gloss and context information to improve performance. |
| Approach: | They propose a poly-encoder architecture which uses the sense gloss to improve WSD performance. |
| Outcome: | The proposed approach outperforms the state-of-the-art in word sense disambiguation by 1.9 F1 points and on the PARSEME 1.1 English dataset. |
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. |
Framing Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration (2021.eacl-main)
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| Challenge: | Recent studies treat Word Sense Disambiguation (WSD) as a single-label classification problem, but gold data suggests that maximizing the probability of a singular sense is not the most suitable training objective for WSD. |
| Approach: | They propose to use Word Sense Disambiguation (WSD) as a multi-label classification problem in which multiple senses can be assigned to each target word. |
| Outcome: | The proposed method bears closer resemblance to how human annotators disambiguate text and can be extended to exploit structured knowledge from semantic networks. |
AMuSE-WSD: An All-in-one Multilingual System for Easy Word Sense Disambiguation (2021.emnlp-demo)
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| Challenge: | Word Sense Disambiguation (WSD) is a task of associating a word in context with its most appropriate sense from a predefined sense inventory. |
| Approach: | They propose to use a state-of-the-art neural model to integrate WSD into real-world applications. |
| Outcome: | The proposed system offers high-quality sense information in 40 languages through a state-of-the-art neural model for WSD. |