Unsupervised Lexical Substitution with Decontextualised Embeddings (2022.coling-1)
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| Challenge: | Existing methods for lexical substitution using pre-trained language models have some limitations. |
| Approach: | They propose an unsupervised method for lexical substitution using pre-trained language models. |
| Outcome: | The proposed method outperforms baseline models and establishes a state-of-the-art without supervision or fine-tuning. |
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| Challenge: | Lexical substitution ranks substitution candidates from the viewpoint of paraphrasability for a target word in a given sentence. |
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| Challenge: | Existing unsupervised lexical simplification methods only use monolingual data and pre-trained models. |
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| Challenge: | Lexical substitution is the task of generating meaningful substitutes for a word in a given textual context. |
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| Challenge: | Lexical substitution (LS) is an extremely powerful technology that can be used as a backbone of various NLP applications such as writing assistance. |
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| Challenge: | lexical substitution tasks require a system to provide adequate replacements for a word in a given context. |
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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. |
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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. |
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Always Keep your Target in Mind: Studying Semantics and Improving Performance of Neural Lexical Substitution (2020.coling-main)
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| Challenge: | Lexical substitution is a powerful technology used in various NLP applications . it generates plausible words that can replace a given word in a textual context . |
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Analysing Lexical Semantic Change with Contextualised Word Representations (2020.acl-main)
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| Challenge: | Existing studies on lexical semantic change have focused on detecting and characterising word meaning shifts using distributional semantic models. |
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