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Despite the success achieved on various natural language processing tasks,word embeddings are difficult to interpret due to the dense vectorrepresentations. This paper focuses on interpreting the embeddings for variousaspects, including sense separation in the vector dimensions and definitiongeneration. Specifically, given a context together with a target word, ouralgorithm first projects the target word embedding to a high-dimensional sparsevector and picks the specific dimensions that can best explain the semanticmeaning of the target word by the encoded contextual information, where thesense of the target word can be indirectly inferred. Finally, our algorithmapplies an RNN to generate the textual definition of the target word in thehuman readable form, which enables direct interpretation of the correspondingword embedding. This paper also introduces a large and high-qualitycontext-definition dataset that consists of sense definitions together withmultiple example sentences per polysemous word, which is a valuable resourcefor definition modeling and word sense disambiguation. The conductedexperiments show the superior performance in BLEU score and the humanevaluation test.

xSense: Learning Sense-Separated Sparse Representations and Textual Definitions for Explainable Word Sense Networks