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