Instance-Based Neural Dependency Parsing

• URL: http://arxiv.org/abs/2109.13497v1
• Date: Tue, 28 Sep 2021 05:30:52 GMT
• Title: Instance-Based Neural Dependency Parsing
• Authors: Hiroki Ouchi, Jun Suzuki, Sosuke Kobayashi, Sho Yokoi, Tatsuki Kuribayashi, Masashi Yoshikawa, Kentaro Inui
• Abstract summary: 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.
• Score: 56.63500180843504
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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