Discourse Coherence in the Wild: A Dataset, Evaluation and Methods

Alice Lai; Joel Tetreault

Discourse Coherence in the Wild: A Dataset, Evaluation and Methods

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arxiv 1805.04993 v1 pith:QKYXAJF7 submitted 2018-05-14 cs.CL

Alice Lai , Joel Tetreault This is my paper

classification cs.CL

keywords coherencediscourseevaluationmethodsreal-worldtextsaddressalgorithms

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To date there has been very little work on assessing discourse coherence methods on real-world data. To address this, we present a new corpus of real-world texts (GCDC) as well as the first large-scale evaluation of leading discourse coherence algorithms. We show that neural models, including two that we introduce here (SentAvg and ParSeq), tend to perform best. We analyze these performance differences and discuss patterns we observed in low coherence texts in four domains.

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