Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition
Hiroki Ouchi, Jun Suzuki, Sosuke Kobayashi, Sho Yokoi, Tatsuki Kuribayashi, Ryuto Konno, Kentaro Inui
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.- Anthology ID:
- 2020.acl-main.575
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6452–6459
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2020.acl-main.575/
- DOI:
- 10.18653/v1/2020.acl-main.575
- Cite (ACL):
- Hiroki Ouchi, Jun Suzuki, Sosuke Kobayashi, Sho Yokoi, Tatsuki Kuribayashi, Ryuto Konno, and Kentaro Inui. 2020. Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6452–6459, Online. Association for Computational Linguistics.
- Cite (Informal):
- Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition (Ouchi et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2020.acl-main.575.pdf
- Code
- hiroki13/instance-based-ner
- Data
- CoNLL 2003