Abstract
Generating explanations for neural networks has become crucial for their applications in real-world with respect to reliability and trustworthiness. In natural language processing, existing methods usually provide important features which are words or phrases selected from an input text as an explanation, but ignore the interactions between them. It poses challenges for humans to interpret an explanation and connect it to model prediction. In this work, we build hierarchical explanations by detecting feature interactions. Such explanations visualize how words and phrases are combined at different levels of the hierarchy, which can help users understand the decision-making of black-box models. The proposed method is evaluated with three neural text classifiers (LSTM, CNN, and BERT) on two benchmark datasets, via both automatic and human evaluations. Experiments show the effectiveness of the proposed method in providing explanations that are both faithful to models and interpretable to humans.- Anthology ID:
- 2020.acl-main.494
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5578–5593
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.494
- DOI:
- 10.18653/v1/2020.acl-main.494
- Cite (ACL):
- Hanjie Chen, Guangtao Zheng, and Yangfeng Ji. 2020. Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5578–5593, Online. Association for Computational Linguistics.
- Cite (Informal):
- Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection (Chen et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2020.acl-main.494.pdf
- Code
- UVa-NLP/HEDGE + additional community code
- Data
- IMDb Movie Reviews, SST