Transformation Networks for Target-Oriented Sentiment Classification

Xin Li, Lidong Bing, Wai Lam, Bei Shi


Abstract
Target-oriented sentiment classification aims at classifying sentiment polarities over individual opinion targets in a sentence. RNN with attention seems a good fit for the characteristics of this task, and indeed it achieves the state-of-the-art performance. After re-examining the drawbacks of attention mechanism and the obstacles that block CNN to perform well in this classification task, we propose a new model that achieves new state-of-the-art results on a few benchmarks. Instead of attention, our model employs a CNN layer to extract salient features from the transformed word representations originated from a bi-directional RNN layer. Between the two layers, we propose a component which first generates target-specific representations of words in the sentence, and then incorporates a mechanism for preserving the original contextual information from the RNN layer.
Anthology ID:
P18-1087
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
946–956
Language:
URL:
https://aclanthology.org/P18-1087
DOI:
10.18653/v1/P18-1087
Bibkey:
Cite (ACL):
Xin Li, Lidong Bing, Wai Lam, and Bei Shi. 2018. Transformation Networks for Target-Oriented Sentiment Classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 946–956, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Transformation Networks for Target-Oriented Sentiment Classification (Li et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-1087.pdf
Presentation:
 P18-1087.Presentation.pdf
Video:
 https://vimeo.com/285801308
Code
 lixin4ever/TNet
Data
SemEval 2014 Task 4 Sub Task 2