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        <title>Interpretable Multi-dataset Evaluation for Named Entity Recognition</title>
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        <namePart type="given">Jinlan</namePart>
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            <namePart type="given">Bonnie</namePart>
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            <namePart type="given">Trevor</namePart>
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            <namePart type="given">Yang</namePart>
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    <abstract>With the proliferation of models for natural language processing tasks, it is even harder to understand the differences between models and their relative merits. Simply looking at differences between holistic metrics such as accuracy, BLEU, or F1 does not tell us why or how particular methods perform differently and how diverse datasets influence the model design choices. In this paper, we present a general methodology for interpretable evaluation for the named entity recognition (NER) task. The proposed evaluation method enables us to interpret the differences in models and datasets, as well as the interplay between them, identifying the strengths and weaknesses of current systems. By making our analysis tool available, we make it easy for future researchers to run similar analyses and drive progress in this area: https://github.com/neulab/InterpretEval</abstract>
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%0 Conference Proceedings
%T Interpretable Multi-dataset Evaluation for Named Entity Recognition
%A Fu, Jinlan
%A Liu, Pengfei
%A Neubig, Graham
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F fu-etal-2020-interpretable
%X With the proliferation of models for natural language processing tasks, it is even harder to understand the differences between models and their relative merits. Simply looking at differences between holistic metrics such as accuracy, BLEU, or F1 does not tell us why or how particular methods perform differently and how diverse datasets influence the model design choices. In this paper, we present a general methodology for interpretable evaluation for the named entity recognition (NER) task. The proposed evaluation method enables us to interpret the differences in models and datasets, as well as the interplay between them, identifying the strengths and weaknesses of current systems. By making our analysis tool available, we make it easy for future researchers to run similar analyses and drive progress in this area: https://github.com/neulab/InterpretEval
%R 10.18653/v1/2020.emnlp-main.489
%U https://aclanthology.org/2020.emnlp-main.489
%U https://doi.org/10.18653/v1/2020.emnlp-main.489
%P 6058-6069

Markdown (Informal)

[Interpretable Multi-dataset Evaluation for Named Entity Recognition](https://aclanthology.org/2020.emnlp-main.489) (Fu et al., EMNLP 2020)

• Interpretable Multi-dataset Evaluation for Named Entity Recognition (Fu et al., EMNLP 2020)

ACL

• Jinlan Fu, Pengfei Liu, and Graham Neubig. 2020. Interpretable Multi-dataset Evaluation for Named Entity Recognition. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6058–6069, Online. Association for Computational Linguistics.