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Creators/Authors contains: "Jordan Boyd-Graber, Fenfei Guo"

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  1. (, Proceedings of the 12th Language Resources and Evaluation Conference)
    null (Ed.)
    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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    Accepted Manuscript Full Text Available