Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension

Ekta Sood, Simon Tannert, Diego Frassinelli, Andreas Bulling, Ngoc Thang Vu


Abstract
While neural networks with attention mechanisms have achieved superior performance on many natural language processing tasks, it remains unclear to which extent learned attention resembles human visual attention. In this paper, we propose a new method that leverages eye-tracking data to investigate the relationship between human visual attention and neural attention in machine reading comprehension. To this end, we introduce a novel 23 participant eye tracking dataset - MQA-RC, in which participants read movie plots and answered pre-defined questions. We compare state of the art networks based on long short-term memory (LSTM), convolutional neural models (CNN) and XLNet Transformer architectures. We find that higher similarity to human attention and performance significantly correlates to the LSTM and CNN models. However, we show this relationship does not hold true for the XLNet models – despite the fact that the XLNet performs best on this challenging task. Our results suggest that different architectures seem to learn rather different neural attention strategies and similarity of neural to human attention does not guarantee best performance.
Anthology ID:
2020.conll-1.2
Volume:
Proceedings of the 24th Conference on Computational Natural Language Learning
Month:
November
Year:
2020
Address:
Online
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
12–25
Language:
URL:
https://aclanthology.org/2020.conll-1.2
DOI:
10.18653/v1/2020.conll-1.2
Bibkey:
Cite (ACL):
Ekta Sood, Simon Tannert, Diego Frassinelli, Andreas Bulling, and Ngoc Thang Vu. 2020. Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension. In Proceedings of the 24th Conference on Computational Natural Language Learning, pages 12–25, Online. Association for Computational Linguistics.
Cite (Informal):
Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension (Sood et al., CoNLL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.conll-1.2.pdf
Data
MovieQA