All Information is Valuable: Question Matching over Full Information Transmission Network

Le Qi, Yu Zhang, Qingyu Yin, Guidong Zheng, Wen Junjie, Jinlong Li, Ting Liu


Abstract
Question matching is the task of identifying whether two questions have the same intent. For better reasoning the relationship between questions, existing studies adopt multiple interaction modules and perform multi-round reasoning via deep neural networks. In this process, there are two kinds of critical information that are commonly employed: the representation information of original questions and the interactive information between pairs of questions. However, previous studies tend to transmit only one kind of information, while failing to utilize both kinds of information simultaneously. To address this problem, in this paper, we propose a Full Information Transmission Network (FITN) that can transmit both representation and interactive information together in a simultaneous fashion. More specifically, we employ a novel memory-based attention for keeping and transmitting the interactive information through a global interaction matrix. Besides, we apply an original-average mixed connection method to effectively transmit the representation information between different reasoning rounds, which helps to preserve the original representation features of questions along with the historical hidden features. Experiments on two standard benchmarks demonstrate that our approach outperforms strong baseline models.
Anthology ID:
2022.findings-naacl.107
Volume:
Findings of the Association for Computational Linguistics: NAACL 2022
Month:
July
Year:
2022
Address:
Seattle, United States
Editors:
Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1431–1440
Language:
URL:
https://aclanthology.org/2022.findings-naacl.107
DOI:
10.18653/v1/2022.findings-naacl.107
Bibkey:
Cite (ACL):
Le Qi, Yu Zhang, Qingyu Yin, Guidong Zheng, Wen Junjie, Jinlong Li, and Ting Liu. 2022. All Information is Valuable: Question Matching over Full Information Transmission Network. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1431–1440, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
All Information is Valuable: Question Matching over Full Information Transmission Network (Qi et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-naacl.107.pdf
Software:
 2022.findings-naacl.107.software.zip
Video:
 https://aclanthology.org/2022.findings-naacl.107.mp4