Challenge: Existing word embeddings cannot model the dynamic nature of words’ semantics, i.e., the property of words to correspond to potentially different meanings.
Approach: They propose a large-scale Word in Context dataset, called WiC, which is curated by experts and can be used to evaluate context-sensitive representations.
Outcome: The proposed models outperform the standard evaluation dataset for the purpose and highlight their shortcomings.

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Challenge: Existing evaluation benchmarks for assessing distinct meanings of words are tied to sense inventories, restricting their usage to knowledge-based representation techniques.
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WiC-TSV: An Evaluation Benchmark for Target Sense Verification of Words in Context (2021.eacl-main)

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Challenge: Existing benchmarks for Word Sense Disambiguation are limited to those systems in which sense distinctions are defined according to an underlying sense inventory.
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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.
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Injecting Wiktionary to improve token-level contextual representations using contrastive learning (2024.eacl-short)

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Challenge: lexical semantics tasks require contextual word embeddings that are not blind to context, despite the fact that vectors of the same meaning are too different.
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With More Contexts Comes Better Performance: Contextualized Sense Embeddings for All-Round Word Sense Disambiguation (2020.emnlp-main)

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Challenge: Contextualized word embeddings have been used effectively across several tasks in Natural Language Processing, but it is difficult to link them to structured sources of knowledge.
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Dynamic Contextualized Word Embeddings (2021.acl-long)

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Challenge: Static word embeddings that represent words by a single vector cannot capture word meaning in different linguistic and extralinguistic contexts.
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Exploring the Value of Personalized Word Embeddings (2020.coling-main)

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Challenge: a subset of words belonging to specific psycholinguistic categories vary more in their representations across users . combining generic and personalized word embeddings yields the best performance .
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Enhancing the Context Representation in Similarity-based Word Sense Disambiguation (2021.emnlp-main)

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Challenge: Existing similarity-based systems focus on learning sense embeddings using only the sentence where the word appears, neglecting its global context.
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A Systematic Comparison of Contextualized Word Embeddings for Lexical Semantic Change (2024.naacl-long)

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Challenge: Contextualized embeddings are the preferred tool for modeling Lexical Semantic Change (LSC) current evaluations focus on a specific task known as Graded Change Detection (GCD) however, performance comparisons between different approaches are often misleading due to diverse settings.
Approach: They evaluate the performance of contextualized embeddings for Lexical Semantic Change (LSC) they break the problem into Word-in-Context (WiC) and Word Sense Induction (WSI) tasks .
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Reassessing Semantic Knowledge Encoded in Large Language Models through the Word-in-Context Task (2024.lrec-main)

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Challenge: Recent advances in large language models (LLMs) have propelled significant progress, extending their application across various domains including dialogue systems, text generation, translation systems, and beyond.
Approach: They propose to use the Word-in-Context (WiC) task to reassess the semantic knowledge encoded in large language models (LLMs) they prompt LLMs to generate natural language descriptions that contrast the meanings of the target word in two contextual sentences given in the WiC dataset.
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