Variational Semi-Supervised Aspect-Term Sentiment Analysis via Transformer

Xingyi Cheng, Weidi Xu, Taifeng Wang, Wei Chu, Weipeng Huang, Kunlong Chen, Junfeng Hu


Abstract
Aspect-term sentiment analysis (ATSA) is a long-standing challenge in natural language process. It requires fine-grained semantical reasoning about a target entity appeared in the text. As manual annotation over the aspects is laborious and time-consuming, the amount of labeled data is limited for supervised learning. This paper proposes a semi-supervised method for the ATSA problem by using the Variational Autoencoder based on Transformer. The model learns the latent distribution via variational inference. By disentangling the latent representation into the aspect-specific sentiment and the lexical context, our method induces the underlying sentiment prediction for the unlabeled data, which then benefits the ATSA classifier. Our method is classifier-agnostic, i.e., the classifier is an independent module and various supervised models can be integrated. Experimental results are obtained on the SemEval 2014 task 4 and show that our method is effective with different the five specific classifiers and outperforms these models by a significant margin.
Anthology ID:
K19-1090
Volume:
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
961–969
Language:
URL:
https://www.aclweb.org/anthology/K19-1090
DOI:
10.18653/v1/K19-1090
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PDF:
http://aclanthology.lst.uni-saarland.de/K19-1090.pdf