When and Why a Model Fails? A Human-in-the-loop Error Detection Framework for Sentiment Analysis
Zhe Liu, Yufan Guo, Jalal Mahmud
Abstract
Although deep neural networks have been widely employed and proven effective in sentiment analysis tasks, it remains challenging for model developers to assess their models for erroneous predictions that might exist prior to deployment. Once deployed, emergent errors can be hard to identify in prediction run-time and impossible to trace back to their sources. To address such gaps, in this paper we propose an error detection framework for sentiment analysis based on explainable features. We perform global-level feature validation with human-in-the-loop assessment, followed by an integration of global and local-level feature contribution analysis. Experimental results show that, given limited human-in-the-loop intervention, our method is able to identify erroneous model predictions on unseen data with high precision.
BibTeX
@inproceedings{liu-etal-2021-model,
title = "When and Why a Model Fails? A Human-in-the-loop Error Detection Framework for Sentiment Analysis",
author = "Liu, Zhe and
Guo, Yufan and
Mahmud, Jalal",
editor = "Kim, Young-bum and
Li, Yunyao and
Rambow, Owen",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-industry.22/",
doi = "10.18653/v1/2021.naacl-industry.22",
pages = "170--177"
}