Attentional Encoder Network for Targeted Sentiment Classification

Abstract

Targeted sentiment classification aims at determining the sentimental tendency towards specific targets. Most of the previous approaches model context and target words with RNN and attention. However, RNNs are difficult to parallelize and truncated backpropagation through time brings difficulty in remembering long-term patterns. To address this issue, this paper proposes an Attentional Encoder Network (AEN) which eschews recurrence and employs attention based encoders for the modeling between context and target. We raise the label unreliability issue and introduce label smoothing regularization. We also apply pre-trained BERT to this task and obtain new state-of-the-art results. Experiments and analysis demonstrate the effectiveness and lightweight of our model.

arXiv comment7 pages

References in corpus (3)

• Adam: A Method for Stochastic Optimization
• Neural Machine Translation by Jointly Learning to Align and Translate
• Effective LSTMs for Target-Dependent Sentiment Classification

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• Investigating Typed Syntactic Dependencies for Targeted Sentiment Classification Using Graph Attention Neural Network
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• Towards Target-dependent Sentiment Classification in News Articles
• Which Review Aspect Has a Greater Impact on the Duration of Open Peer Review in Multiple Rounds? -- Evidence from Nature Communications
• Antibody Watch: Text Mining Antibody Specificity from the Literature
• Enhancing Fine-grained Sentiment Classification Exploiting Local Context Embedding