Papers with LS07
LexSubCon: Integrating Knowledge from Lexical Resources into Contextual Embeddings for Lexical Substitution (2022.acl-long)
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| Challenge: | Lexical substitution is the task of generating meaningful substitutes for a word in a given textual context. |
| Approach: | They propose an end-to-end lexical substitution framework based on contextual embedding models that can identify highly-accurate substitute candidates. |
| Outcome: | The proposed framework outperforms state-of-the-art embedding models on LS07 and CoInCo benchmark datasets by at least 2% over existing embeddable models. |
BERT-based Lexical Substitution (P19-1)
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| Challenge: | Existing approaches to lexical substitution tend to overlook good substitute candidates that are not the synonyms of the target words in the lexicals and fail to take into account the substitution’s influence on the global context of the sentence. |
| Approach: | They propose an end-to-end BERT-based lexical substitution approach which proposes and validates substitute candidates without using annotated data or manually curated resources. |
| Outcome: | The proposed approach performs well in proposing and ranking substitute candidates, achieving the state-of-the-art results in both LS07 and LS14 benchmarks. |
CILex: An Investigation of Context Information for Lexical Substitution Methods (2022.coling-1)
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| Challenge: | Existing methods for lexical substitution rely on manually curated lexicals and contextual word embedding models. |
| Approach: | They propose a method that uses contextual sentence embeddings to generate substitutes for a target word given a context and a model that captures additional context information complimenting contextual word embedders. |
| Outcome: | The proposed method is state-of-the-art on the widely used LS07 and CoInCo datasets with P@1 scores of 55.96% and 57.25% for lexical substitution. |