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    <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.</abstract>
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%0 Conference Proceedings
%T Variational Semi-Supervised Aspect-Term Sentiment Analysis via Transformer
%A Cheng, Xingyi
%A Xu, Weidi
%A Wang, Taifeng
%A Chu, Wei
%A Huang, Weipeng
%A Chen, Kunlong
%A Hu, Junfeng
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F cheng-etal-2019-variational
%X 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.
%R 10.18653/v1/K19-1090
%U https://aclanthology.org/K19-1090
%U https://doi.org/10.18653/v1/K19-1090
%P 961-969

Markdown (Informal)

[Variational Semi-Supervised Aspect-Term Sentiment Analysis via Transformer](https://aclanthology.org/K19-1090) (Cheng et al., CoNLL 2019)

• Variational Semi-Supervised Aspect-Term Sentiment Analysis via Transformer (Cheng et al., CoNLL 2019)

ACL

• Xingyi Cheng, Weidi Xu, Taifeng Wang, Wei Chu, Weipeng Huang, Kunlong Chen, and Junfeng Hu. 2019. Variational Semi-Supervised Aspect-Term Sentiment Analysis via Transformer. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 961–969, Hong Kong, China. Association for Computational Linguistics.