Which Evaluations Uncover Sense Representations that Actually Make Sense?
Text representations are critical for modern natural language processing. One form of text representation, sense-specific embeddings, reflect a word{'}s sense in a sentence better than single-prototype word embeddings tied to each type. However, existing sense representations are not uniformly better: although they work well for computer-centric evaluations, they fail for human-centric tasks like inspecting a language{'}s sense inventory. To expose this discrepancy, we propose a new coherence evaluation for sense embeddings. We also describe a minimal model (Gumbel Attention for Sense Induction) optimized for discovering interpretable sense representations that are more coherent than existing sense embeddings.
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