Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning

Xuantao Lu, Jingping Liu, Zhouhong Gu, Hanwen Tong, Chenhao Xie, Junyang Huang, Yanghua Xiao, Wenguang Wang


Abstract
Semantic parsing converts natural language utterances into structured logical expressions. We consider two such formal representations: Propositional Logic (PL) and First-order Logic (FOL). The paucity of labeled data is a major challenge in this field. In previous works, dual reinforcement learning has been proposed as an approach to reduce dependence on labeled data. However, this method has the following limitations: 1) The reward needs to be set manually and is not applicable to all kinds of logical expressions. 2) The training process easily collapses when models are trained with only the reward from dual reinforcement learning. In this paper, we propose a scoring model to automatically learn a model-based reward, and an effective training strategy based on curriculum learning is further proposed to stabilize the training process. In addition to the technical contribution, a Chinese-PL/FOL dataset is constructed to compensate for the paucity of labeled data in this field. Experimental results show that the proposed method outperforms competitors on several datasets. Furthermore, by introducing PL/FOL generated by our model, the performance of existing Natural Language Inference (NLI) models is further enhanced.
Anthology ID:
2022.coling-1.481
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
5419–5431
Language:
URL:
https://aclanthology.org/2022.coling-1.481
DOI:
Bibkey:
Cite (ACL):
Xuantao Lu, Jingping Liu, Zhouhong Gu, Hanwen Tong, Chenhao Xie, Junyang Huang, Yanghua Xiao, and Wenguang Wang. 2022. Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5419–5431, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning (Lu et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.481.pdf
Data
SNLI