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• W3156587088 abstract "Lexical inference in context (LIiC) is the task of recognizing textual entailment between two very similar sentences, i.e., sentences that only differ in one expression. It can therefore be seen as a variant of the natural language inference task that is focused on lexical semantics. We formulate and evaluate the first approaches based on pretrained language models (LMs) for this task: (i) a few-shot NLI classifier, (ii) a relation induction approach based on handcrafted patterns expressing the semantics of lexical inference, and (iii) a variant of (ii) with patterns that were automatically extracted from a corpus. All our approaches outperform the previous state of the art, showing the potential of pretrained LMs for LIiC. In an extensive analysis, we investigate factors of success and failure of our three approaches." @default.
• W3156587088 created "2021-04-26" @default.
• W3156587088 creator A5037086889 @default.
• W3156587088 creator A5071144367 @default.
• W3156587088 date "2021-01-01" @default.
• W3156587088 modified "2023-09-24" @default.
• W3156587088 title "Language Models for Lexical Inference in Context" @default.
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• W3156587088 doi "https://doi.org/10.18653/v1/2021.eacl-main.108" @default.
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