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Lexical substitution is the task of generating meaningful substitutes for aword in a given textual context. Contextual word embedding models have achievedstate-of-the-art results in the lexical substitution task by relying oncontextual information extracted from the replaced word within the sentence.However, such models do not take into account structured knowledge that existsin external lexical databases.  We introduce LexSubCon, an end-to-end lexical substitution framework based oncontextual embedding models that can identify highly accurate substitutecandidates. This is achieved by combining contextual information with knowledgefrom structured lexical resources. Our approach involves: (i) introducing anovel mix-up embedding strategy in the creation of the input embedding of thetarget word through linearly interpolating the pair of the target inputembedding and the average embedding of its probable synonyms; (ii) consideringthe similarity of the sentence-definition embeddings of the target word and itsproposed candidates; and, (iii) calculating the effect of each substitution inthe semantics of the sentence through a fine-tuned sentence similarity model.Our experiments show that LexSubCon outperforms previous state-of-the-artmethods on LS07 and CoInCo benchmark datasets that are widely used for lexicalsubstitution tasks.

LexSubCon: Integrating Knowledge from Lexical Resources into Contextual Embeddings for Lexical Substitution