@inproceedings{ouchi-etal-2020-instance,
title = "Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition",
author = "Ouchi, Hiroki and
Suzuki, Jun and
Kobayashi, Sosuke and
Yokoi, Sho and
Kuribayashi, Tatsuki and
Konno, Ryuto and
Inui, Kentaro",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.575",
doi = "10.18653/v1/2020.acl-main.575",
pages = "6452--6459",
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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<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.</abstract>
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%0 Conference Proceedings
%T Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition
%A Ouchi, Hiroki
%A Suzuki, Jun
%A Kobayashi, Sosuke
%A Yokoi, Sho
%A Kuribayashi, Tatsuki
%A Konno, Ryuto
%A Inui, Kentaro
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F ouchi-etal-2020-instance
%X 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.
%R 10.18653/v1/2020.acl-main.575
%U https://aclanthology.org/2020.acl-main.575
%U https://doi.org/10.18653/v1/2020.acl-main.575
%P 6452-6459
Markdown (Informal)
[Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition](https://aclanthology.org/2020.acl-main.575) (Ouchi et al., ACL 2020)
ACL