@inproceedings{88ac45ed31754d999a25e7253ef2b7b3,
title = "Incorporating Option and Out-of-domain Knowledge for Multi-choice Machine Reading Comprehension",
abstract = "Multi-choice Machine Reading Comprehension (MRC) requires the model to select the correct answer from a set of answer candidates given the corresponding passage and question. Previous studies mainly focus on complex matching networks to model the relationship among options, passage and question. However, these models obtain little improvement over the powerful Pre-trained Language Models (PLMs). In this paper, we propose a simple method to incorporate option knowledge from PLMs and introduce out-of-domain knowledge by multi-task learning skillfully. Our approach obtains state-of-the-art results on Chinese multi-choice MRC dataset ReCO and also effectively improves the performance on C3.",
keywords = "Multi-choice MRC, Multi-task Learning, Option Knowledge",
author = "Yuan Xu and Shumin Shi and Heyan Huang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 7th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2021 ; Conference date: 07-11-2021 Through 08-11-2021",
year = "2021",
doi = "10.1109/CCIS53392.2021.9754687",
language = "English",
series = "Proceedings of 2021 7th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "493--497",
editor = "Deyi Li and Mengqi Zhou and Weining Wang and Yaru Zou and Meng Luo and Qian Zhang",
booktitle = "Proceedings of 2021 7th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2021",
address = "United States",
}