Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network
Published 1 January 2018Open access
Jianfei Yu, Luís Marujo, Jing Jiang, Pradeep Karuturi, William Brendel
Citations74
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TL;DR
A new transfer learning architecture is proposed to divide the sentence representation into two different feature spaces, which are expected to respectively capture the general sentiment words and the other important emotion-specific words via a dual attention mechanism.
Abstract
In this paper, we target at improving the performance of multi-label emotion classification with the help of sentiment classification. Specifically, we propose a new transfer learning architecture to divide the sentence representation into two different feature spaces, which are expected to respectively capture the general sentiment words and the other important emotion-specific words via a dual attention mechanism. Extensive experimental results demonstrate that our transfer learning approach can outperform several strong baselines and achieve the state-of-the-art performance on two benchmark datasets.
Keywords
Computer Science
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