Textbook Question Answering Under Instructor Guidance With Memory Networks

Juzheng Li, Hang Su, Jun Zhu, Siyu Wang, Bo Zhang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 3655-3663

Abstract


Textbook Question Answering (TQA) is a task to choose the most proper answers by reading a multi-modal context of abundant essays and images. TQA serves as a favorable test bed for visual and textual reasoning. However, most of the current methods are incapable of reasoning over the long contexts and images. To address this issue, we propose a novel approach of Instructor Guidance with Memory Networks (IGMN) which conducts the TQA task by finding contradictions between the candidate answers and their corresponding context. We build the Contradiction Entity-Relationship Graph (CERG) to extend the passage-level multi-modal contradictions to an essay level. The machine thus performs as an instructor to extract the essay-level contradictions as the Guidance. Afterwards, we exploit the memory networks to capture the information in the Guidance, and use the attention mechanisms to jointly reason over the global features of the multi-modal input. Extensive experiments demonstrate that our method outperforms the state-of-the-arts on the TQA dataset. The source code is available at https://github.com/freerailway/igmn.

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[bibtex]
@InProceedings{Li_2018_CVPR,
author = {Li, Juzheng and Su, Hang and Zhu, Jun and Wang, Siyu and Zhang, Bo},
title = {Textbook Question Answering Under Instructor Guidance With Memory Networks},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}