Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition

• URL: http://arxiv.org/abs/2004.14514v1
• Date: Wed, 29 Apr 2020 23:32:42 GMT
• Title: Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition
• Authors: Hiroki Ouchi, Jun Suzuki, Sosuke Kobayashi, Sho Yokoi, Tatsuki Kuribayashi, Ryuto Konno, Kentaro Inui
• Abstract summary: We present a method of instance-based learning that learns similarities between spans. Our method enables to build models that have high interpretability without sacrificing performance.
• Score: 48.06319154279427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Interpretable rationales for model predictions play a critical role in practical applications. In this study, we develop models possessing interpretable inference process for structured prediction. Specifically, we present a method of instance-based learning that learns similarities between spans. At inference time, each span is assigned a class label based on its similar spans in the training set, where it is easy to understand how much each training instance contributes to the predictions. Through empirical analysis on named entity recognition, we demonstrate that our method enables to build models that have high interpretability without sacrificing performance.

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