Calibrating Structured Output Predictors for Natural Language Processing

Abhyuday Jagannatha, Hong Yu


Abstract
We address the problem of calibrating prediction confidence for output entities of interest in natural language processing (NLP) applications. It is important that NLP applications such as named entity recognition and question answering produce calibrated confidence scores for their predictions, especially if the applications are to be deployed in a safety-critical domain such as healthcare. However the output space of such structured prediction models are often too large to directly adapt binary or multi-class calibration methods. In this study, we propose a general calibration scheme for output entities of interest in neural network based structured prediction models. Our proposed method can be used with any binary class calibration scheme and a neural network model. Additionally, we show that our calibration method can also be used as an uncertainty-aware, entity-specific decoding step to improve the performance of the underlying model at no additional training cost or data requirements. We show that our method outperforms current calibration techniques for Named Entity Recognition, Part-of-speech tagging and Question Answering systems. We also observe an improvement in model performance from our decoding step across several tasks and benchmark datasets. Our method improves the calibration and model performance on out-of-domain test scenarios as well.
Anthology ID:
2020.acl-main.188
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2078–2092
Language:
URL:
https://aclanthology.org/2020.acl-main.188
DOI:
10.18653/v1/2020.acl-main.188
Bibkey:
Cite (ACL):
Abhyuday Jagannatha and Hong Yu. 2020. Calibrating Structured Output Predictors for Natural Language Processing. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2078–2092, Online. Association for Computational Linguistics.
Cite (Informal):
Calibrating Structured Output Predictors for Natural Language Processing (Jagannatha & Yu, ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.188.pdf
Video:
 http://slideslive.com/38929172
Data
emrQA