Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering

Quan Hung Tran, Nhan Dam, Tuan Lai, Franck Dernoncourt, Trung Le, Nham Le, Dinh Phung


Abstract
Interpretability and explainability of deep neural net models are always challenging due to their size and complexity. Many previous works focused on visualizing internal components of neural networks to represent them through human-friendly concepts. On the other hand, in real life, when making a decision, human tends to rely on similar situations in the past. Thus, we argue that one potential approach to make the model interpretable and explainable is to design it in a way such that the model explicitly connects the current sample with the seen samples, and bases its decision on these samples. In this work, we design one such model: an explainable, evidence-based memory network architecture, which learns to summarize the dataset and extract supporting evidences to make its decision. The model achieves state-of-the-art performance on two popular question answering datasets, the TrecQA dataset and the WikiQA dataset. Via further analysis, we showed that this model can reliably trace the errors it has made in the validation step to the training instances that might have caused this error. We believe that this error-tracing capability might be beneficial in improving dataset quality in many applications.
Anthology ID:
2020.coling-main.456
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
5205–5210
Language:
URL:
https://aclanthology.org/2020.coling-main.456
DOI:
10.18653/v1/2020.coling-main.456
Bibkey:
Cite (ACL):
Quan Hung Tran, Nhan Dam, Tuan Lai, Franck Dernoncourt, Trung Le, Nham Le, and Dinh Phung. 2020. Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5205–5210, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering (Tran et al., COLING 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2020.coling-main.456.pdf
Data
TrecQAWikiQA