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        <title>Transformation Networks for Target-Oriented Sentiment Classification</title>
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        <namePart type="given">Xin</namePart>
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            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
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            <namePart type="given">Yusuke</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Transformation Networks for Target-Oriented Sentiment Classification
%A Li, Xin
%A Bing, Lidong
%A Lam, Wai
%A Shi, Bei
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F li-etal-2018-transformation
%X 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.
%R 10.18653/v1/P18-1087
%U https://aclanthology.org/P18-1087/
%U https://doi.org/10.18653/v1/P18-1087
%P 946-956

Markdown (Informal)

[Transformation Networks for Target-Oriented Sentiment Classification](https://aclanthology.org/P18-1087/) (Li et al., ACL 2018)

• Transformation Networks for Target-Oriented Sentiment Classification (Li et al., ACL 2018)

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.