Sentiment Interpretable Logic Tensor Network for Aspect-Term Sentiment Analysis

Bowen Zhang, Xu Huang, Zhichao Huang, Hu Huang, Baoquan Zhang, Xianghua Fu, Liwen Jing


Abstract
Aspect-term sentiment analysis (ATSA) is an important task that aims to infer the sentiment towards the given aspect-terms. It is often required in the industry that ATSA should be performed with interpretability, computational efficiency and high accuracy. However, such an ATSA method has not yet been developed. This study aims to develop an ATSA method that fulfills all these requirements. To achieve the goal, we propose a novel Sentiment Interpretable Logic Tensor Network (SILTN). SILTN is interpretable because it is a neurosymbolic formalism and a computational model that supports learning and reasoning about data with a differentiable first-order logic language (FOL). To realize SILTN with high inferring accuracy, we propose a novel learning strategy called the two-stage syntax knowledge distillation (TSynKD). Using widely used datasets, we experimentally demonstrate that the proposed TSynKD is effective for improving the accuracy of SILTN, and the SILTN has both high interpretability and computational efficiency.
Anthology ID:
2022.coling-1.582
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:
6705–6714
Language:
URL:
https://aclanthology.org/2022.coling-1.582
DOI:
Bibkey:
Cite (ACL):
Bowen Zhang, Xu Huang, Zhichao Huang, Hu Huang, Baoquan Zhang, Xianghua Fu, and Liwen Jing. 2022. Sentiment Interpretable Logic Tensor Network for Aspect-Term Sentiment Analysis. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6705–6714, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Sentiment Interpretable Logic Tensor Network for Aspect-Term Sentiment Analysis (Zhang et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.582.pdf