Transactions of the Association for Computational Linguistics (Jan 2021)

Evaluating Document Coherence Modeling

  • Aili Shen,
  • Meladel Mistica,
  • Bahar Salehi,
  • Hang Li,
  • Timothy Baldwin,
  • Jianzhong Qi

DOI
https://doi.org/10.1162/tacl_a_00388
Journal volume & issue
Vol. 9
pp. 621 – 640

Abstract

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AbstractWhile pretrained language models (LMs) have driven impressive gains over morpho-syntactic and semantic tasks, their ability to model discourse and pragmatic phenomena is less clear. As a step towards a better understanding of their discourse modeling capabilities, we propose a sentence intrusion detection task. We examine the performance of a broad range of pretrained LMs on this detection task for English. Lacking a dataset for the task, we introduce INSteD, a novel intruder sentence detection dataset, containing 170,000+ documents constructed from English Wikipedia and CNN news articles. Our experiments show that pretrained LMs perform impressively in in-domain evaluation, but experience a substantial drop in the cross-domain setting, indicating limited generalization capacity. Further results over a novel linguistic probe dataset show that there is substantial room for improvement, especially in the cross- domain setting.