TY - GEN
T1 - Learn to Combine Linguistic and Symbolic Information for Table-based Fact Verification
AU - Shi, Qi
AU - Zhang, Yu
AU - Yin, Qingyu
AU - Liu, Ting
N1 - Publisher Copyright:
© 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.
PY - 2020
Y1 - 2020
N2 - Table-based fact verification is expected to perform both linguistic reasoning and symbolic reasoning. Existing methods lack attention to take advantage of the combination of linguistic information and symbolic information. In this work, we propose HeterTFV, a graph-based reasoning approach, that learns to combine linguistic information and symbolic information effectively. We first construct a program graph to encode programs, a kind of LISP-like logical form, to learn the semantic compositionality of the programs. Then we construct a heterogeneous graph to incorporate both linguistic information and symbolic information by introducing program nodes into the heterogeneous graph. Finally, we propose a graph-based reasoning approach to reason over the multiple types of nodes to make an effective combination of both types of information. Experimental results on a large-scale benchmark dataset TABFACT illustrate the effect of our approach.
AB - Table-based fact verification is expected to perform both linguistic reasoning and symbolic reasoning. Existing methods lack attention to take advantage of the combination of linguistic information and symbolic information. In this work, we propose HeterTFV, a graph-based reasoning approach, that learns to combine linguistic information and symbolic information effectively. We first construct a program graph to encode programs, a kind of LISP-like logical form, to learn the semantic compositionality of the programs. Then we construct a heterogeneous graph to incorporate both linguistic information and symbolic information by introducing program nodes into the heterogeneous graph. Finally, we propose a graph-based reasoning approach to reason over the multiple types of nodes to make an effective combination of both types of information. Experimental results on a large-scale benchmark dataset TABFACT illustrate the effect of our approach.
UR - https://www.scopus.com/pages/publications/85107308301
U2 - 10.18653/v1/2020.coling-main.466
DO - 10.18653/v1/2020.coling-main.466
M3 - 会议稿件
AN - SCOPUS:85107308301
T3 - COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference
SP - 5335
EP - 5346
BT - COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference
A2 - Scott, Donia
A2 - Bel, Nuria
A2 - Zong, Chengqing
PB - Association for Computational Linguistics (ACL)
T2 - 28th International Conference on Computational Linguistics, COLING 2020
Y2 - 8 December 2020 through 13 December 2020
ER -