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    Using rationales and influential training examples to (attempt to) explain neural predictions in NLP

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    Date
    2020-09-09
    Author
    Wallace, Byron
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    Abstract
    Modern deep learning models for natural language processing (NLP) achieve state-of-the-art predictive performance but are notoriously opaque. I will discuss recent work looking to address this limitation. I will focus specifically on approaches to: (i) Providing snippets of text (sometimes called "rationales") that support predictions, and; (ii) Identifying examples from the training data that influenced a given model output.
    URI
    http://hdl.handle.net/1853/63705
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    • Machine Learning@Georgia Tech Seminars [52]

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