Towards Interpreting BERT for Reading Comprehension Based QA

Sahana Ramnath, Preksha Nema, Deep Sahni, Mitesh M. Khapra


Abstract
BERT and its variants have achieved state-of-the-art performance in various NLP tasks. Since then, various works have been proposed to analyze the linguistic information being captured in BERT. However, the current works do not provide an insight into how BERT is able to achieve near human-level performance on the task of Reading Comprehension based Question Answering. In this work, we attempt to interpret BERT for RCQA. Since BERT layers do not have predefined roles, we define a layer’s role or functionality using Integrated Gradients. Based on the defined roles, we perform a preliminary analysis across all layers. We observed that the initial layers focus on query-passage interaction, whereas later layers focus more on contextual understanding and enhancing the answer prediction. Specifically for quantifier questions (how much/how many), we notice that BERT focuses on confusing words (i.e., on other numerical quantities in the passage) in the later layers, but still manages to predict the answer correctly. The fine-tuning and analysis scripts will be publicly available at https://github.com/iitmnlp/BERT-Analysis-RCQA.
Anthology ID:
2020.emnlp-main.261
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:
3236–3242
Language:
URL:
https://aclanthology.org/2020.emnlp-main.261
DOI:
10.18653/v1/2020.emnlp-main.261
Bibkey:
Cite (ACL):
Sahana Ramnath, Preksha Nema, Deep Sahni, and Mitesh M. Khapra. 2020. Towards Interpreting BERT for Reading Comprehension Based QA. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3236–3242, Online. Association for Computational Linguistics.
Cite (Informal):
Towards Interpreting BERT for Reading Comprehension Based QA (Ramnath et al., EMNLP 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/update-css-js/2020.emnlp-main.261.pdf
Video:
 https://slideslive.com/38939356
Code
 iitmnlp/BERT-Analysis-RCQA
Data
DuoRC