Papers › SentiInc: Incorporating Sentiment Information into Sentiment Transfer Without Parallel Data

SentiInc: Incorporating Sentiment Information into Sentiment Transfer Without Parallel Data

8 Apr 2020European Conference on Information Retrieval 2020 4archive 2025-07-28

Kartikey Pant, Yash Verma, Radhika Mamidi

Sentiment-to-sentiment transfer involves changing the sentiment of the given text while preserving the underlying information. In this work, we present a model SentiInc for sentiment-to-sentiment transfer using unpaired mono-sentiment data. Existing sentiment-to-sentiment transfer models ignore the valuable sentiment-specific details already present in the text. We address this issue by providing a simple framework for encoding sentiment-specific information in the target sentence while preserving the content information. This is done by incorporating sentiment based loss in the back-translation based style transfer. Extensive experiments over the Yelp dataset show that the SentiInc outperforms state-of-the-art methods by a margin of as large as equation ~11% in G-score. The results also demonstrate that our model produces sentiment-accurate and information-preserved sentences.

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