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Creators/Authors contains: "Jordan Boyd-Graber"

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  1. ; ; (, Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing)
    Accepted Manuscript Full Text Available
  2. (, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing)
    Question answering (QA) primarily descends from two branches of research: (1) Alan Turing’s investigation of machine intelligence at Manchester University and (2) Cyril Cleverdon’s comparison of library card catalog indices at Cranfield University. This position paper names and distinguishes these paradigms. Despite substantial overlap, subtle but significant distinctions exert an outsize influence on research. While one evaluation paradigm values creating more intelligent QA systems, the other paradigm values building QA systems that appeal to users. By better understanding the epistemic heritage of QA, researchers, academia, and industry can more effectively accelerate QA research. 
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    Full Text Available 
  3. (, 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