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

Understand

Despite the success achieved on various natural language processing tasks, word embeddings are difficult to interpret due to the dense vector representations.

• This paper focuses on interpreting the embeddings for various aspects, including sense separation in the vector dimensions and definition generation.
• Specifically, given a context together with a target word, our algorithm first projects the target word embedding to a high-dimensional sparse vector and picks the specific dimensions that can best explain the semantic meaning of the target word by the encoded contextual information, where the sense of the target word can be indirectly inferred.
• Finally, our algorithm applies an RNN to generate the textual definition of the target word in the human readable form, which enables direct interpretation of the corresponding word embedding.