Instance-based neural dependency parsing

Hiroki Ouchi, Jun Suzuki, Sosuke Kobayashi, Sho Yokoi, Tatsuki Kuribayashi, Masashi Yoshikawa, Kentaro Inui

Research output: Contribution to journalArticlepeer-review

Abstract

Interpretable rationales for model predictions are crucial in practical applications. We develop neural models that possess an interpretable inference process for dependency parsing. Our models adopt instance-based inference, where dependency edges are extracted and labeled by comparing them to edges in a training set. The training edges are explicitly used for the predictions; thus, it is easy to grasp the contribution of each edge to the predictions. Our experiments show that our instance-based models achieve competitive accuracy with standard neural models and have the reasonable plausibility of instance-based explanations.

Original languageEnglish
Pages (from-to)1493-1507
Number of pages15
JournalTransactions of the Association for Computational Linguistics
Volume9
DOIs
Publication statusPublished - 2021 Dec 17

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Computer Science Applications
  • Linguistics and Language
  • Communication

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