Unsupervised Aspect-Level Sentiment Controllable Style Transfer
Unsupervised style transfer in text has previously been explored through the sentiment transfer task. The task entails inverting the overall sentiment polarity in a given input sentence, while preserving its content. From the Aspect-Based Sentiment Analysis (ABSA) task, we know that multiple sentiment polarities can often be present together in a sentence with multiple aspects. In this paper, the task of aspect-level sentiment controllable style transfer is introduced, where each of the aspect-level sentiments can individually be controlled at the output. To achieve this goal, a BERT-based encoder-decoder architecture with saliency weighted polarity injection is proposed, with unsupervised training strategies, such as ABSA masked-language-modelling. Through both automatic and manual evaluation, we show that the system is successful in controlling aspect-level sentiments.
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Tasks
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)DecoderLanguage ModellingSentenceSentiment AnalysisStyle TransferSimilar Papers 제목 키워드 기반
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