A Multi-Stage Memory Augmented Neural Network for Machine Reading Comprehension
Seunghak Yu, Sathish Reddy Indurthi, Seohyun Back, Haejun Lee
Abstract
Reading Comprehension (RC) of text is one of the fundamental tasks in natural language processing. In recent years, several end-to-end neural network models have been proposed to solve RC tasks. However, most of these models suffer in reasoning over long documents. In this work, we propose a novel Memory Augmented Machine Comprehension Network (MAMCN) to address long-range dependencies present in machine reading comprehension. We perform extensive experiments to evaluate proposed method with the renowned benchmark datasets such as SQuAD, QUASAR-T, and TriviaQA. We achieve the state of the art performance on both the document-level (QUASAR-T, TriviaQA) and paragraph-level (SQuAD) datasets compared to all the previously published approaches.- Anthology ID:
- W18-2603
- Volume:
- Proceedings of the Workshop on Machine Reading for Question Answering
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Eunsol Choi, Minjoon Seo, Danqi Chen, Robin Jia, Jonathan Berant
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 21–30
- Language:
- URL:
- https://aclanthology.org/W18-2603
- DOI:
- 10.18653/v1/W18-2603
- Cite (ACL):
- Seunghak Yu, Sathish Reddy Indurthi, Seohyun Back, and Haejun Lee. 2018. A Multi-Stage Memory Augmented Neural Network for Machine Reading Comprehension. In Proceedings of the Workshop on Machine Reading for Question Answering, pages 21–30, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- A Multi-Stage Memory Augmented Neural Network for Machine Reading Comprehension (Yu et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/W18-2603.pdf
- Data
- QUASAR-T, SQuAD