WSD

Metaphorical Polysemy Detection: Conventional Metaphor Meets Word Sense Disambiguation (2022.coling-1)

Challenge:	SyntagRank is a knowledge-based WSD system that exploits syntagmatic information to perform state-of-the-art knowledge-driven WSD in a multilingual setting.
Approach:	They propose to exploit syntagmatic information to perform state-of-the-art knowledge-based WSD in a multilingual setting by using a Web interface and a RESTful API.
Outcome:	SyntagRank exploits disambiguated pairs of words in SyntagNet to perform state-of-the-art knowledge-based WSD in a multilingual setting.

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Rowan Hall Maudslay, Simone Teufel

Sentiment Analysis

Challenge:	Linguists distinguish between novel and conventional metaphors, a distinction which the metaphor detection task in NLP does not take into account.
Approach:	They propose a method which treats conventional metaphors as a property of word senses in a lexicon and combines metaphor detection with word sense disambiguation to train it.
Outcome:	The proposed model outperforms a state-of-the-art model in annotating metaphor in two subsets of WordNet and achieves .78 ROC-AUC score compared to baseline model .

MWE as WSD: Solving Multiword Expression Identification with Word Sense Disambiguation (2023.findings-emnlp)

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Joshua Tanner, Jacob Hoffman

Zuo Zhuan Ancient Chinese Dataset for Word Sense Disambiguation (2022.naacl-srw)

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.

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Xiaomeng Pan, Hongfei Wang, Teruaki Oka, Mamoru Komachi

A Deep Dive into Word Sense Disambiguation with LSTM (C18-1)

Challenge:	Word Sense Disambiguation (WSD) is a core task in natural language processing . ancient Chinese has rarely been used in WSD tasks due to lack of a dataset .
Approach:	They annotate ancient Chinese text Zuo Zhuan using a copyright-free dictionary . they apply a method to find the most appropriate sense in a context using k-NN .
Outcome:	The proposed dataset will be available on GitHub.

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Minh Le, Marten Postma, Jacopo Urbani, Piek Vossen

AMuSE-WSD: An All-in-one Multilingual System for Easy Word Sense Disambiguation (2021.emnlp-demo)

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.
Outcome:	The proposed method returned state-of-the-art performance in several benchmarks, but neither the training data nor the source code were released.

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Riccardo Orlando, Simone Conia, Fabrizio Brignone, Francesco Cecconi, Roberto Navigli

FEWS: Large-Scale, Low-Shot Word Sense Disambiguation with the Dictionary (2021.eacl-main)

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.

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Terra Blevins, Mandar Joshi, Luke Zettlemoyer

Query Generation Using GPT-3 for CLIP-Based Word Sense Disambiguation for Image Retrieval (2023.starsem-1)

Challenge:	Existing models for Word Sense Disambiguation struggle to disambiguate rare senses . current models struggle to learn senses with few training examples .
Approach:	They introduce a low-shot WSD dataset automatically extracted from example sentences in Wiktionary.
Outcome:	The proposed dataset outperforms baseline models on rare senses in existing datasets.

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Xiaomeng Pan, Zhousi Chen, Mamoru Komachi

Generative Dictionary: Improving Language Learner Understanding with Contextual Definitions (2024.emnlp-demo)

Challenge:	Existing studies show that human-like prompts with quotes benefit both CLIP and GPT-3 as implicit word sense disambiguation components.
Approach:	They propose using the GPT-3 as a query generator for the backend of CLIP as an implicit word sense disambiguation component for the SemEval 2023 shared task Visual Word Sense Disambiguation.
Outcome:	The proposed query generator for CLIP is an implicit word sense disambiguation component for the SemEval 2023 shared task Visual Word Sense Disambiguation (VWSD). human-like prompts adapted for WSD with quotes benefit both CLIP and GPT-3, whereas plain phrases or poorly templated prompts give the worst results.

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Kai-Wen Tuan, Hai-Lun Tu, Jason Chang

Towards General-Domain Word Sense Disambiguation: Distilling Large Language Model into Compact Disambiguator (2025.emnlp-main)

Challenge:	GenerativeDictionary generates word sense interpretations based on context . traditional word sense disambiguation methods may not capture the intended word sense .
Approach:	They propose a dictionary system that generates word sense interpretations based on context . they transform context sentences to highlight the meaning of target words .
Outcome:	The proposed dictionary system is comparable to traditional word sense disambiguation methods.

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Liqiang Ming, Sheng-hua Zhong, Yuncong Li

Do Pretrained Contextual Language Models Distinguish between Hebrew Homograph Analyses? (2023.eacl-main)

Challenge:	Existing methods for Word Sense Disambiguation rely heavily on manually annotated data, which limits coverage and generalization.
Approach:	They propose a framework that leverages large language models as knowledge distillers to build silver-standard WSD corpora by combining generation-based distillation and annotation-based disambiguation.
Outcome:	The proposed framework outperforms existing methods on general-domain benchmarks by 50% on the most challenging test set and by 1000 times fewer parameters.

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Avi Shmidman, Cheyn Shmuel Shmidman, Dan Bareket, Moshe Koppel, Reut Tsarfaty

Just “OneSeC” for Producing Multilingual Sense-Annotated Data (P19-1)

Challenge:	Semitic morphologically-rich languages are characterized by extreme word ambiguity . many of the words are homographs with multiple possible analyses .
Approach:	They evaluate existing models for Hebrew homographs using word-piece embeddings . they find they are more effective when the number of word-part splits is limited .
Outcome:	The proposed models outperform non-contextualized embeddings on Hebrew homograph challenge sets.

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Bianca Scarlini, Tommaso Pasini, Roberto Navigli

Potential Idiomatic Expression (PIE)-English: Corpus for Classes of Idioms (2022.lrec-1)

Challenge:	Word Sense Disambiguation (WSD) is one of the most affected research areas . annotated data are scarce in English and almost absent in other languages .
Approach:	They propose a language-independent method for the automatic extraction of thousands of sentences in which a target word is tagged with its meaning.
Outcome:	The proposed method outperforms existing methods on multilingual and domain-specific settings.

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Tosin Adewumi, Roshanak Vadoodi, Aparajita Tripathy, Konstantina Nikolaido, Foteini Liwicki, Marcus Liwicki

Information Retrieval

Challenge:	Potential Idiomatic Expression (PIE) dataset for NLP in English contains over 20,100 samples with almost 1,200 cases of idioms from 10 classes (or senses).
Approach:	They present a large Potential Idiomatic Expression (PIE) dataset for Natural Language Processing (NLP) in English.
Outcome:	The proposed dataset contains over 20,100 samples with almost 1,200 cases of idioms (with their meanings) from 10 classes (or senses).

Leveraging Word-Formation Knowledge for Chinese Word Sense Disambiguation (2021.findings-emnlp)

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Hua Zheng, Lei Li, Damai Dai, Deli Chen, Tianyu Liu, Xu Sun, Yang Liu

Analyzing Homonymy Disambiguation Capabilities of Pretrained Language Models (2024.lrec-main)

Challenge:	Word sense disambiguation (WSD) methods have not explored word-formations in parataxis languages like Chinese.
Approach:	They propose to leverage word-formation knowledge to enhance Chinese WSD by incorporating word-forms into sense disambiguation models.
Outcome:	The proposed model improves on baselines in Chinese word sense disambiguation (WSD) with word-formation knowledge, the results show.

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Lorenzo Proietti, Stefano Perrella, Simone Tedeschi, Giulia Vulpis, Leonardo Lavalle, Andrea Sanchietti, Andrea Ferrari, Roberto Navigli

Sense-Aware Neural Models for Pun Location in Texts (P18-2)

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.

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Yitao Cai, Yin Li, Xiaojun Wan

Translate to Disambiguate: Zero-shot Multilingual Word Sense Disambiguation with Pretrained Language Models (2024.eacl-long)

Challenge:	Puns where the two meanings share the same pronunciation are known as homographic puns.
Approach:	They propose a sense-aware neural model to address the task of pun location . they first obtain several WSD results for the text and then leverage a bidirectional LSTM network to model each word senses.
Outcome:	The proposed model is based on a SemEval 2017 benchmark dataset showing that it can predict homographic puns.

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Haoqiang Kang, Terra Blevins, Luke Zettlemoyer

Moving Down the Long Tail of Word Sense Disambiguation with Gloss Informed Bi-encoders (2020.acl-main)

Challenge:	Pretrained language models learn cross-lingual knowledge and perform well on diverse tasks when finetuned.
Approach:	They propose a zero-shot prompting approach that captures cross-lingual word sense with a contextual prompt.
Outcome:	The proposed approach outperforms baselines on recall in many evaluation languages without additional training or finetuning.

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Terra Blevins, Luke Zettlemoyer

Adversarial Multi-task Learning for End-to-end Metaphor Detection (2023.findings-acl)

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.

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Shenglong Zhang, Ying Liu

SMSMix: Sense-Maintained Sentence Mixup for Word Sense Disambiguation (2022.findings-emnlp)

Challenge:	Existing methods to learn basic sense discrimination (BSD) are limited in training data.
Approach:	They propose a multi-task learning framework to transfer MD knowledge to basic sense discrimination using word sense disambiguation.
Outcome:	The proposed framework can mitigate the data scarcity problem in metaphor detection.

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Hee Suk Yoon, Eunseop Yoon, John Harvill, Sunjae Yoon, Mark Hasegawa-Johnson, Chang Yoo

ConSeC: Word Sense Disambiguation as Continuous Sense Comprehension (2021.emnlp-main)

Challenge:	Word Sense Disambiguation (WSD) is an NLP task aimed at determining the correct sense of a word in a sentence from discrete sense choices.
Approach:	They propose a word-level mixup method that maintains the sense of a target word by increasing the frequency of least frequent senses during training to reduce the distributional bias of senses.
Outcome:	The proposed method shows high accuracy on the most frequent sense (MFS) and low accuracy on least frequent senses (LFS).

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Edoardo Barba, Luigi Procopio, Roberto Navigli

How Much Do Encoder Models Know About Word Senses? (2025.acl-long)

Challenge:	Existing systems for word Sense Disambiguation assume that each word can be disambiguated individually . a novel approach to WSD is proposed to address this limitation .
Approach:	They propose a supervised semantics-based approach to Word Sense Disambiguation that takes into account the senses assigned to nearby words.
Outcome:	The proposed approach surpasses all its competitors and sets a new state of the art on English WSD.

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Simone Teglia, Simone Tedeschi, Roberto Navigli

LTRS: Improving Word Sense Disambiguation via Learning to Rank Senses (2025.coling-main)

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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Hansi Wang, Yue Wang, Qiliang Liang, Yang Liu

Non-Parametric Few-Shot Learning for Word Sense Disambiguation (2021.naacl-main)

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.

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Howard Chen, Mengzhou Xia, Danqi Chen

Try to Substitute: An Unsupervised Chinese Word Sense Disambiguation Method Based on HowNet (2020.coling-main)

Challenge:	Word sense disambiguation (WSD) is a problem in natural language processing . 84% of annotated words have less than 10 examples in the long-tail distribution .
Approach:	They propose a non-parametric few-shot learning approach to mitigate word sense disambiguation . they use a metric space to compute distances among the senses of a given word .
Outcome:	The proposed method achieves a 75.1 F1 score on the unified evaluation benchmark.

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Bairu Hou, Fanchao Qi, Yuan Zang, Xurui Zhang, Zhiyuan Liu, Maosong Sun

Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources (L18-1)

Challenge:	Existing unsupervised methods for word sense disambiguation cannot work for HowNet-based WSD because of its uniqueness.
Approach:	They propose a method which exploits the masked language model task of pre-trained language models to conduct word sense disambiguation using a lexical knowledge base as the sense inventory.
Outcome:	The proposed method achieves significantly better performance than baseline methods.

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Stefano Melacci, Achille Globo, Leonardo Rigutini

Together We Make Sense–Learning Meta-Sense Embeddings (2023.findings-acl)

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.

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Haochen Luo, Yi Zhou, Danushka Bollegala

Unsupervised Korean Word Sense Disambiguation using CoreNet (L18-1)

Challenge:	Existing sense embeddings do not cover all senses of ambiguous words equally well due to discrepancies in their training resources.
Approach:	They propose a meta-sense embedding method that preserves sense neighbourhoods by combining multiple independently trained source sense embeddables.
Outcome:	The proposed method outperforms several baselines on Word Sense Disambiguation and Word-in-Context tasks.

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Kijong Han, Sangha Nam, Jiseong Kim, Younggyun Hahm, Key-Sun Choi

FastSense: An Efficient Word Sense Disambiguation Classifier (L18-1)

Challenge:	Unsupervised learning based Korean word sense disambiguation is needed to distinguish between sense candidates.
Approach:	They investigated unsupervised Korean word sense disambiguation using CoreNet, a Korean lexical semantic network.
Outcome:	The proposed method exhibited an 80.9% accuracy on the datasets constructed and proved to be effective for practical applications.

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Tolga Uslu, Alexander Mehler, Daniel Baumartz, Wahed Hemati

Leveraging Gloss Knowledge in Neural Word Sense Disambiguation by Hierarchical Co-Attention (D18-1)

Challenge:	Word Sense Disambiguation (WSD) is a task that is often overlooked by NLP pipelines because of its complexity and complexity.
Approach:	They propose a neural network-based tool for word sense disambiguation called fastSense.
Outcome:	The proposed tool can process huge amounts of data quickly and surpasses state-of-the-art tools in terms of F-measure.

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Fuli Luo, Tianyu Liu, Zexue He, Qiaolin Xia, Zhifang Sui, Baobao Chang

Measuring Context-Word Biases in Lexical Semantic Datasets (2022.emnlp-main)

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.

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Qianchu Liu, Diana McCarthy, Anna Korhonen

Combating the Curse of Multilinguality in Cross-Lingual WSD by Aligning Sparse Contextualized Word Representations (2022.naacl-main)

Challenge:	Existing pretrained contextualized models have been used to evaluate word-in-context representations in many lexical semantic tasks.
Approach:	They propose to quantify the degree of context or word biases in existing datasets by probing masked input.
Outcome:	The proposed model performs better when both word and context are available than with masked input.

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Gábor Berend

WiC = TSV = WSD: On the Equivalence of Three Semantic Tasks (2022.naacl-main)

Challenge:	Existing approaches to handle knowledge acquisition bottlenecks in multilingual training are limited due to the curse of multilinguality.
Approach:	They propose to use large pre-trained monolingual language models in cross lingual zero-shot word sense disambiguation coupled with a contextualized mapping mechanism.
Outcome:	The proposed model improves the average F-score by nearly 6.5 points over 17 target languages.

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Bradley Hauer, Grzegorz Kondrak

Challenge:	Word-in-Context (WiC) task has attracted considerable attention in the NLP community, as demonstrated by the popularity of the recent MCL-Wic SemEval shared task.
Approach:	They propose to use lexical resources from word sense disambiguation and target sense verification to reduce the relationship between the two tasks.
Outcome:	The proposed methods can be pairwise reduced to each other and therefore work in practice.

Word sense extension (2023.acl-long)

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Lei Yu, Yang Xu

On the Curious Case of l2 norm of Sense Embeddings (2022.findings-emnlp)

Challenge:	a long-standing effort in natural language processing has focused on word sense disambiguation, but little has been explored about how word meaning is extended toward new context.
Approach:	They propose a framework that partitions a word type into two pseudo-tokens that mark its different senses and infers whether the meaning can be extended to convey the sense denoted by the token.
Outcome:	The proposed framework outperforms other models in predicting plausible novel senses for over 7,500 English words.

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Yi Zhou, Danushka Bollegala

Word Sense Disambiguation Based on Word Similarity Calculation Using Word Vector Representation from a Knowledge-based Graph (C18-1)

Challenge:	l2 norm of sense embeddings encodes information related to frequency of that sense in the training corpus . l2-normal feature is useful for word-in-context (WiC) and word sense disambiguation (WSD)
Approach:	They propose to include the l2 norm of a sense embedding as a feature in a classifier to improve word sense learning methods that use static sense embeds.
Outcome:	The l2 norm of sense embeddings is a surprisingly effective feature for word sense related tasks such as word-in-context (WiC) and word sense disambiguation (WSD).

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Dongsuk O, Sunjae Kwon, Kyungsun Kim, Youngjoong Ko

Incorporating Glosses into Neural Word Sense Disambiguation (P18-1)

Challenge:	Existing methods to determine the word sense have fewer word set constraints than unsupervised methods.
Approach:	They propose a new word sense disambiguation method that generates the context of an ambiguous word by using similarities between an amorphous word and words in the input document.
Outcome:	The proposed method outperforms the baseline method on the SemEval-13 and SemEx15 datasets and has higher performance than the state-of-the-art unsupervised knowledge-based system.

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Fuli Luo, Tianyu Liu, Qiaolin Xia, Baobao Chang, Zhifang Sui

Can Word Sense Distribution Detect Semantic Changes of Words? (2023.findings-emnlp)

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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Xiaohang Tang, Yi Zhou, Taichi Aida, Procheta Sen, Danushka Bollegala

Ambiguity Meets Uncertainty: Investigating Uncertainty Estimation for Word Sense Disambiguation (2023.findings-acl)

Challenge:	Existing methods to detect semantic variations of words are not accurate for time-sensitive predictions.
Approach:	They propose to use pretrained static sense embeddings to annotate a word's occurrence with a sense id to compare its distributions.
Outcome:	The proposed method compares word sense distributions across two corpora to predict meaning change . the results show that pretrained LLMs can detect changes in words over time .

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Zhu Liu, Ying Liu

Word Sense Disambiguation

Challenge:	Existing supervised methods treat word sense disambiguation as a classification task but ignore uncertainty estimation (UE) in the real-world setting, the data is always noisy and out of distribution.
Approach:	They propose to use word sense disambiguation to determine an appropriate sense for a word given its context to determine the most appropriate sense.
Outcome:	The proposed model reflects data uncertainty satisfactorily but underestimates model uncertainty.

Semantic Specialization for Knowledge-based Word Sense Disambiguation (2023.eacl-main)

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Sakae Mizuki, Naoaki Okazaki

Breaking Through the 80% Glass Ceiling: Raising the State of the Art in Word Sense Disambiguation by Incorporating Knowledge Graph Information (2020.acl-main)

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.

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Michele Bevilacqua, Roberto Navigli

Don’t Neglect the Obvious: On the Role of Unambiguous Words in Word Sense Disambiguation (2020.emnlp-main)

Challenge:	Neural architectures are the current state of the art in Word Sense Disambiguation (WSD) however, they make limited use of the vast amount of relational information encoded in Lexical Knowledge Bases (LKBs).
Approach:	They propose a neural supervised architecture that embeds Lexical Knowledge Bases and exploits pretrained synset embeddings to predict synsets that are not in the training set.
Outcome:	The proposed architecture breaks through the 80% ceiling on the concatenation of all standard all-words English evaluation benchmarks.

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Daniel Loureiro, Jose Camacho-Collados

Framing Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration (2021.eacl-main)

Challenge:	Existing senseannotated corpora lack coverage of many instances in WordNet . however, unambiguous words make up a large portion of WordNet while being poorly covered in existing senseannnotated .
Approach:	They propose a method to provide annotations for most unambiguous words in a large corpus by using a dataset.
Outcome:	The proposed method improves on the original results on Word Sense Disambiguation (WSD) using pre-trained language models and propagation algorithms.

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Simone Conia, Roberto Navigli

Information Extraction & Text Mining

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.

FOOL ME IF YOU CAN! An Adversarial Dataset to Investigate the Robustness of LMs in Word Sense Disambiguation (2024.emnlp-main)

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Mohamad Ballout, Anne Dedert, Nohayr Abdelmoneim, Ulf Krumnack, Gunther Heidemann, Kai-Uwe Kühnberger

Rare and Zero-shot Word Sense Disambiguation using Z-Reweighting (2022.acl-long)

Challenge:	Word sense disambiguation (WSD) is a key task in natural language processing . however, these models struggle with recognizing semantic boundaries in adversarial contexts .
Approach:	They propose to use a coarse-grained WSD dataset to assess model robustness . they found that some models struggled to correctly disambiguate homonyms in adversarial contexts .
Outcome:	The proposed dataset includes four test sets to assess the robustness of language models in WSD tasks.

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Ying Su, Hongming Zhang, Yangqiu Song, Tong Zhang

Nibbling at the Hard Core of Word Sense Disambiguation (2022.acl-long)

Challenge:	Word sense disambiguation (WSD) is a problem in the natural language processing community.
Approach:	They propose a method to adjust training on imbalanced word sense dataset . they propose to achieve performance gain on standard English all words benchmark .
Outcome:	The proposed method achieves performance gain on the standard English all words benchmark.

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Marco Maru, Simone Conia, Michele Bevilacqua, Roberto Navigli

Improving HowNet-Based Chinese Word Sense Disambiguation with Translations (2022.findings-emnlp)

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.

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Xiang Zhang, Bradley Hauer, Grzegorz Kondrak

Improving Word Sense Disambiguation with Translations (2020.emnlp-main)

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.

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Yixing Luan, Bradley Hauer, Lili Mou, Grzegorz Kondrak

ContrastWSD: Enhancing Metaphor Detection with Word Sense Disambiguation Following the Metaphor Identification Procedure (2024.lrec-main)

Challenge:	Existing WSD systems rarely consider multilingual information for word sense disambiguation (WSD).
Approach:	They propose a method that leverages multilingual information to improve a base WSD system by generating translations.
Outcome:	The proposed method improves performance of a base WSD system in English and multilingual WSD on several languages.

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Mohamad MZ Elzohbi, Richard Zhao

Sentiment Analysis

Challenge:	Existing methods for identifying metaphoric expressions in text relied on manual effort to identify the basic and contextual meanings of words.
Approach:	They propose a model that integrates the Metaphor Identification Procedure (MIP) and Word Sense Disambiguation (WSD) to extract and contrast the contextual meaning with the basic meaning of a word to determine whether it is used metaphorically in a sentence.
Outcome:	The proposed model outperforms methods that rely on embeddings or integrate only basic definitions and other external knowledge.

GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge (D19-1)

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Luyao Huang, Chi Sun, Xipeng Qiu, Xuanjing Huang

Word Sense Disambiguation with Knowledge-Enhanced and Local Self-Attention-based Extractive Sense Comprehension (2022.coling-1)

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.

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Guobiao Zhang, Wenpeng Lu, Xueping Peng, Shoujin Wang, Baoshuo Kan, Rui Yu

SANDWiCH: Semantical Analysis of Neighbours for Disambiguating Words in Context ad Hoc (2025.naacl-long)

Challenge:	Word sense disambiguation (WSD) is one of the most challenging tasks in natural language processing.
Approach:	They propose a method to extract the right sense from a sentence context . they propose to incorporate additional examples and definitions of related senses in WordNet .
Outcome:	The proposed method achieves better performance than baseline models on public benchmark datasets.

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Daniel Guzman Olivares, Lara Quijano, Federico Liberatore

Reasoning

Challenge:	Recent studies show that language understanding offered by chat-based Large Language Models is limited and far from human-like performance.
Approach:	They propose a framework for multilingual Word Sense Disambiguation using group algebra.
Outcome:	The proposed framework surpasses the performance of current alternatives even in low-resource languages while reducing the parameter count by 72%.

SyntagNet: Challenging Supervised Word Sense Disambiguation with Lexical-Semantic Combinations (D19-1)

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Marco Maru, Federico Scozzafava, Federico Martelli, Roberto Navigli

Improved Word Sense Disambiguation with Enhanced Sense Representations (2021.findings-emnlp)

Challenge:	Current research in knowledge-based Word Sense Disambiguation (WSD) indicates that performances depend heavily on the Lexical Knowledge Base (LKB) employed.
Approach:	They propose to use a Lexical Knowledge Base to capture syntagmatic relations to enable knowledge-based WSD systems to achieve a new state of the art.
Outcome:	The proposed resource captures syntagmatic relations and is the first large-scale manually-curated resource of this kind made available to the community.

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Yang Song, Xin Cai Ong, Hwee Tou Ng, Qian Lin

Multilingual Word Sense Disambiguation with Unified Sense Representation (2022.coling-1)

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.

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Ying Su, Hongming Zhang, Yangqiu Song, Tong Zhang

CluBERT: A Cluster-Based Approach for Learning Sense Distributions in Multiple Languages (2020.acl-main)

Challenge:	Existing researches on word sense disambiguation focus on English only.
Approach:	They propose to build knowledge and supervised based multilingual word sense disambiguation systems on a multilingual lexicon describing the same set of concepts across languages.
Outcome:	The proposed model can understand the fine-grained semantics of words under specific contexts.

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Tommaso Pasini, Federico Scozzafava, Bianca Scarlini

ESC: Redesigning WSD with Extractive Sense Comprehension (2021.naacl-main)

Challenge:	Existing methods to induce word senses from raw sentences lack reliable and high-coverage distributions.
Approach:	They propose an automatic and multilingual approach to inducing word senses from a corpus of raw sentences using an annotated corpus.
Outcome:	The proposed method outperforms all other methods on English and other languages.

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Edoardo Barba, Tommaso Pasini, Roberto Navigli

Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation (2020.findings-emnlp)

Challenge:	Word Sense Disambiguation (WSD) is a historical NLP task aimed at linking words in contexts to discrete sense inventories.
Approach:	They propose a transformer-based neural architecture for extractive Sense Comprehension to solve a span extraction problem and a new state of the art English WSD task.
Outcome:	The proposed model outdoes all of its competitors while relying on three times fewer annotations.

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Nithin Holla, Pushkar Mishra, Helen Yannakoudakis, Ekaterina Shutova

Word Sense Disambiguation: Towards Interactive Context Exploitation from Both Word and Sense Perspectives (2021.acl-long)

Challenge:	Existing methods for word sense disambiguation (WSD) are limited and require large datasets annotated with word senses.
Approach:	They propose a meta-learning framework for few-shot word sense disambiguation where the goal is to learn to disambiguate unseen words from only a few labeled instances.
Outcome:	The proposed framework is based on a large training dataset and a small number of examples.

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Ming Wang, Yinglin Wang

Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation (2021.acl-long)

Challenge:	Recent Word Sense Disambiguation systems have approached the upper bound of the task on standard evaluation benchmarks.
Approach:	They propose to convert the nearly isolated decisions into interrelated ones by exposing senses in context when learning sense embeddings in a similarity-based Sense Aware Context Exploitation architecture.
Outcome:	The proposed approach surpasses state-of-the-art on English and multilingual datasets by large margins.

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Yingjun Du, Nithin Holla, Xiantong Zhen, Cees Snoek, Ekaterina Shutova

Semi-Automated Construction of Sense-Annotated Datasets for Practically Any Language (2025.coling-main)

Challenge:	Existing methods for word sense disambiguation (WSD) lack large annotated datasets with sufficient coverage of words . performance of such methods lags behind fully-supervised methods . a meta-learning model is proposed to solve this problem .
Approach:	They propose a model of semantic memory for supervised word sense disambiguation using meta-learning.
Outcome:	The proposed model improves performance in few-shot WSD and produces meaning prototypes that capture similar senses of distinct words.

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Jai Riley, Bradley M. Hauer, Nafisa Sadaf Hriti, Guoqing Luo, Amir Reza Mirzaei, Ali Rafiei, Hadi Sheikhi, Mahvash Siavashpour, Mohammad Tavakoli, Ning Shi, Grzegorz Kondrak

An Evaluation Benchmark for Testing the Word Sense Disambiguation Capabilities of Machine Translation Systems (2020.lrec-1)

Challenge:	Word sense disambiguation is a widely studied NLP task of identifying the meaning of a word in context.
Approach:	They propose a method to create parallel sense-annotated datasets in English . they use machine translation, word alignment, sense projection, and sense filtering to produce silver annotations .
Outcome:	The proposed method produces parallel sense-annotated datasets on Farsi, Chinese, and Bengali . the results are higher than those obtained with recent multilingual systems, the authors say .

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Alessandro Raganato, Yves Scherrer, Jörg Tiedemann

A Synset Relation-enhanced Framework with a Try-again Mechanism for Word Sense Disambiguation (2020.emnlp-main)

Challenge:	Lexical ambiguity is one of the many challenging linguistic phenomena involved in translation, i.e., translating an ambiguous word with its correct sense.
Approach:	They propose to use training data to measure the sense distributions of a machine translation system to measure lexical ambiguity.
Outcome:	The proposed benchmark builds upon the multilingual sense inventory of BabelNet, the multilinguistic neural parsing pipeline TurkuNLP, and the OPUS collection of translated texts from the web.

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Ming Wang, Yinglin Wang

Weiyi Lu, Thien Huu Nguyen

Challenge:	Existing sense embeddings fail to embed sense knowledge in semantic networks.
Approach:	They propose a Synset Relation-Enhanced Framework that leverages sense relations for sense embedding enhancement and a try-again mechanism that implements WSD again.
Outcome:	The proposed system outperforms knowledge-based systems with 20% SemCor data on all-words and lexical datasets.

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Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representations (D19-1)

Challenge:	Event detection (ED) and word sense disambiguation (WSD) are similar tasks, but they require different neural representations.
Approach:	They propose a method to transfer the knowledge learned on WSD to ED by matching neural representations learned for the two tasks.
Outcome:	The proposed method can be applied to event detection and word sense disambiguation datasets.

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Christian Hadiwinoto, Hwee Tou Ng, Wee Chung Gan

Zero-shot Word Sense Disambiguation using Sense Definition Embeddings (P19-1)

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.

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Sawan Kumar, Sharmistha Jat, Karan Saxena, Partha Talukdar

Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation (P19-1)

Challenge:	Word Sense Disambiguation (WSD) is an open problem in Natural Language Processing . current methods treat senses as discrete labels and predict the most-frequent-Sense for unseen senses .
Approach:	They propose a supervised model to perform Word Sense Disambiguation (WSD) by predicting over a continuous sense embedding space rather than a discrete label space.
Outcome:	The proposed model generalizes over seen and unseen senses, achieving zero-shot learning.

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Daniel Loureiro, Alípio Jorge

PolCLIP: A Unified Image-Text Word Sense Disambiguation Model via Generating Multimodal Complementary Representations (2024.acl-long)

Challenge:	Contextual embeddings address the problem of meaning conflation hampering word embeddables.
Approach:	They propose a method that creates sense-level embeddings with full-coverage of WordNet without recourse to explicit sense distributions or task-specific modelling.
Outcome:	The proposed method surpasses previous systems using powerful models and is robust when ignoring part-of-speech and lemma features.

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Qihao Yang, Yong Li, Xuelin Wang, Fu Lee Wang, Tianyong Hao

Visual Data in NLP

Challenge:	Existing models for word sense disambiguation lack images or senses in textual and visual datasets.
Approach:	They propose a unified image-text WSD model that uses image-sense complementarity to generate visual representations for word senses and a disambiguation-oriented image-sensor dataset to provide implicit textual representations.
Outcome:	The proposed model achieves 2.53% F1-score increase over state-of-the-art models on Textual-WSD and 2.22% HR@1 improvement on Visual-WSS.

Neuro-Symbolic Sentiment Analysis with Dynamic Word Sense Disambiguation (2023.findings-emnlp)

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Xulang Zhang, Rui Mao, Kai He, Erik Cambria

Explainability & Interpretability in NLP

Challenge:	Traditional neural network models represent word senses as vectors that are uninterpretable for humans.
Approach:	They propose a framework that incorporates word Sense Disambiguation (WSD) by identifying and paraphrasing ambiguous words to improve sentiment predictions.
Outcome:	The proposed framework improves sentiment analysis accuracy and interpretability on a downstream task without ground-truth word sense labels.

Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories (2021.emnlp-main)

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Wenlin Yao, Xiaoman Pan, Lifeng Jin, Jianshu Chen, Dian Yu, Dong Yu

Taxonomy of Problems in Lexical Semantics (2023.findings-acl)

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.

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Bradley Hauer, Grzegorz Kondrak

Cross-Lingual Transfer

Challenge:	Semantic tasks are rarely formally defined, and the exact relationship between them is unclear . a taxonomy of several problems in lexical semantics is proposed to clarify this .
Approach:	They propose a taxonomy that elucidates the connection between several problems in lexical semantics . they propose equivalence theory and algorithmic problem reductions to reduce problems to word sense disambiguation (WSD)
Outcome:	The proposed taxonomy proves that word sense disambiguation and word synonymy are theoretically equivalent.

Well Begun is Half Done: Low-resource Preference Alignment by Weak-to-Strong Decoding (2025.findings-acl)

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Feifan Song, Shaohang Wei, Wen Luo, Yuxuan Fan, Tianyu Liu, Guoyin Wang, Houfeng Wang

Enhancing the Context Representation in Similarity-based Word Sense Disambiguation (2021.emnlp-main)

Challenge:	Low-resource methods for LLM alignment have been popular, but still face challenges in obtaining high-quality and aligned content.
Approach:	They propose a framework to enhance alignment ability of base models by the guidance of a small aligned model.
Outcome:	The proposed framework outperforms baseline methods while avoiding degradation on downstream tasks.

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Ming Wang, Jianzhang Zhang, Yinglin Wang

Integrating Personalized PageRank into Neural Word Sense Disambiguation (2021.emnlp-main)

Challenge:	Existing similarity-based systems focus on learning sense embeddings using only the sentence where the word appears, neglecting its global context.
Approach:	They propose a contextoriented embedding technique that takes better advantage of both word-level and sense-level global context of an ambiguous word for disambiguation.
Outcome:	The proposed method improves on all-words WSD benchmarks in knowledge-based category by large margins.

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Ahmed El Sheikh, Michele Bevilacqua, Roberto Navigli

Word Sense Disambiguation

Challenge:	Neural Word Sense Disambiguation (WSD) uses pre-existing knowledge, but only close neighbors influence prediction.
Approach:	They propose to exploit WordNet graphs to improve a classification model by recomputing logits . they incorporate an online neural approximated PageRank to refine edge weights .
Outcome:	The proposed method improves the current state of the art in the field of Neural Word Sense Disambiguation (WSD) the proposed method exploits the global graph structure while keeping space requirements linear in the number of edges.

Sense-Annotated Corpora for Word Sense Disambiguation in Multiple Languages and Domains (2020.lrec-1)

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Bianca Scarlini, Tommaso Pasini, Roberto Navigli

Word Sense Disambiguation for 158 Languages using Word Embeddings Only (2020.lrec-1)

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.

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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

One Classifier for All Ambiguous Words: Overcoming Data Sparsity by Utilizing Sense Correlations Across Words (2020.lrec-1)

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.

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Prafulla Kumar Choubey, Ruihong Huang

Information Extraction & Text Mining

Challenge:	Existing word-specific classifiers lack the ability to generalize across words and require limited sense-annotated data for every word.
Approach:	They propose to learn a single model that derives sense representations and enforces congruence between a word instance and its right sense by using both sense-annotated data and lexical resources.
Outcome:	Empirical evaluation shows the proposed model outperforms classifier-based models by 1.7%, 2.5% and 3.8% in F1-score on GloVe, ELMo and BERT word embeddings respectively.

Word Sense Linking: Disambiguating Outside the Sandbox (2024.findings-acl)

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Andrei Bejgu, Edoardo Barba, Luigi Procopio, Alberte Fernández-Castro, Roberto Navigli

RoDEval: A Robust Word Sense Disambiguation Evaluation Framework for Large Language Models (2025.emnlp-main)

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 .

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Luyang Zhang, Shuaimin Li, Yishuo Li, Kunpeng Kang, Kaiyuan Zhang, Cong Wang, Wenpeng Lu

Language Pivoting from Parallel Corpora for Word Sense Disambiguation of Historical Languages: A Case Study on Latin (2024.lrec-main)

Challenge:	Existing studies rely on single-task evaluations and classification-based metrics that overlook the fundamental differences between generative LLMs and traditional classification models.
Approach:	They propose to use four new metrics to evaluate LLM-based word sense disambiguation (WSD) . experimental results reveal significant limitations in LLMs' WSD performance .
Outcome:	The proposed evaluation framework is open-source at https://github.com/DayDream405/RoDEval.

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Iacopo Ghinassi, Simone Tedeschi, Paola Marongiu, Roberto Navigli, Barbara McGillivray

Morpheme Sense Disambiguation: A New Task Aiming for Understanding the Language at Character Level (2024.lrec-main)

Challenge:	Word Sense Disambiguation (WSD) is an important task in NLP . most of the work on this task has been done on contemporary English or other modern languages, leaving challenges posed by low-resource languages and diachronic change open.
Approach:	They propose to use existing bilingual corpora instead of native English datasets to generate a Latin WSD model.
Outcome:	The proposed approach achieves state-of-the-art on a standard benchmark for Latin WSD.

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Yue Wang, Hua Zheng, Yaqi Yin, Hansi Wang, Qiliang Liang, Yang Liu

Visual Data in NLP

Challenge:	Morphemes are a strong linguistic feature to capture lexical semantics, but lack of morpheme-informed resources and the expense of manual annotations hinder morphme-enhanced methods.
Approach:	They propose a task of Morpheme Sense Disambiguation with two subtasks in-text and in-word to generalize morpheme features on more tasks.
Outcome:	The proposed tasks are based on two morpheme-annotated datasets for Chinese . the best model yields a promising precision of 77.66% on in-text and 88.19% on in word .

MADAWSD: Multi-Agent Debate Framework for Adversarial Word Sense Disambiguation (2025.emnlp-main)

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Kaiyuan Zhang, Qian Liu, Luyang Zhang, Chaoqun Zheng, Shuaimin Li, Bing Xu, Muyun Yang, Xinxiao Qiao, Wenpeng Lu

Robustness in NLP

Challenge:	Word sense disambiguation (WSD) is a fundamental yet challenging task in natural language processing.
Approach:	a novel multi-agent Debate framework for adversarial word Sense disambiguation is proposed . the framework simulates a real-world debate environment where multiple agents engage in discussions about ambiguous words in the context of adversarials.
Outcome:	The proposed framework integrates with existing LLMs and improves models in Chinese language . it shows that it can be used to improve models in the Chinese language and improve performance .

Sense of the Day: Short Timeframe Temporal-Aware Word Sense Disambiguation (2024.lrec-main)

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Yuchen Wei, Milton King

WSDPO: A Generative Word Sense Disambiguation Framework with Chain-of-Thought and Preference Optimization (2026.acl-long)

Challenge:	Existing models that consider the predominant sense of a lemma can be tailored to a specific timeframe and based on the timeframe of the text.
Approach:	They use Twitter to explore whether different senses are favoured within specific timeframes and how they are used to create short timeframe temporal-aware word sense disambiguation models.
Outcome:	The proposed model outperforms temporal agnostic models and author-aware models.

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Kunpeng Kang, Shuaimin Li, Kaiyuan Zhang, Luyang Zhang, Jiasheng Si, Bing Xu, Kehai Chen, Muyun Yang, Wenpeng Lu

Do Large Language Models Understand Word Senses? (2025.emnlp-main)

Challenge:	Word sense disambiguation (WSD) is a fundamental task in natural language processing.
Approach:	They propose a training framework for generative WSD with chain-of-thought (CoT) and preference optimization.
Outcome:	The proposed framework achieves significant performance gains on rare and unseen settings and exhibits strong generalization in standard evaluation settings.

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Domenico Meconi, Simone Stirpe, Federico Martelli, Leonardo Lavalle, Roberto Navigli