Interpretable Multi-dataset Evaluation for Named Entity Recognition

Jinlan Fu, Pengfei Liu, Graham Neubig


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
Anthology ID:
2020.emnlp-main.489
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6058–6069
Language:
URL:
https://www.aclweb.org/anthology/2020.emnlp-main.489
DOI:
10.18653/v1/2020.emnlp-main.489
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.emnlp-main.489.pdf
Optional supplementary material:
 2020.emnlp-main.489.OptionalSupplementaryMaterial.zip