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Instance-Based Neural Dependency Parsing
Open AccessPublisher: Journals Gateway
Transactions of the Association for Computational Linguistics (2021) 9: 1493–1507.
Published: 17 December 2021
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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.