CORN: Co-Reasoning Network for Commonsense Question Answering

Xin Guan, Biwei Cao, Qingqing Gao, Zheng Yin, Bo Liu, Jiuxin Cao


Abstract
Commonsense question answering (QA) requires machines to utilize the QA content and external commonsense knowledge graph (KG) for reasoning when answering questions. Existing work uses two independent modules to model the QA contextual text representation and relationships between QA entities in KG, which prevents information sharing between modules for co-reasoning. In this paper, we propose a novel model, Co-Reasoning Network (CORN), which adopts a bidirectional multi-level connection structure based on Co-Attention Transformer. The structure builds bridges to connect each layer of the text encoder and graph encoder, which can introduce the QA entity relationship from KG to the text encoder and bring contextual text information to the graph encoder, so that these features can be deeply interactively fused to form comprehensive text and graph node representations. Meanwhile, we propose a QA-aware node based KG subgraph construction method. The QA-aware nodes aggregate the question entity nodes and the answer entity nodes, and further guide the expansion and construction process of the subgraph to enhance the connectivity and reduce the introduction of noise. We evaluate our model on QA benchmarks in the CommonsenseQA and OpenBookQA datasets, and CORN achieves state-of-the-art performance.
Anthology ID:
2022.coling-1.144
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Editors:
Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1677–1686
Language:
URL:
https://aclanthology.org/2022.coling-1.144
DOI:
Bibkey:
Cite (ACL):
Xin Guan, Biwei Cao, Qingqing Gao, Zheng Yin, Bo Liu, and Jiuxin Cao. 2022. CORN: Co-Reasoning Network for Commonsense Question Answering. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1677–1686, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
CORN: Co-Reasoning Network for Commonsense Question Answering (Guan et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.144.pdf
Data
CommonsenseQAConceptNetOpenBookQA