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Methods for learning word sense embeddings represent a single word withmultiple sense-specific vectors. These methods should not only produceinterpretable sense embeddings, but should also learn how to select which senseto use in a given context. We propose an unsupervised model that learns senseembeddings using a modified Gumbel softmax function, which allows fordifferentiable discrete sense selection. Our model produces sense embeddingsthat are competitive (and sometimes state of the art) on multiple similaritybased downstream evaluations. However, performance on these downstreamevaluations tasks does not correlate with interpretability of sense embeddings,as we discover through an interpretability comparison with competingmulti-sense embeddings. While many previous approaches perform well ondownstream evaluations, they do not produce interpretable embeddings and learnduplicated sense groups; our method achieves the best of both worlds.

Inducing and Embedding Senses with Scaled Gumbel Softmax