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 ~11% in G-score. The results also demonstrate that our model produces sentiment-accurate and information-preserved sentences.
CITATION STYLE
Pant, K., Verma, Y., & Mamidi, R. (2020). SentiInc: Incorporating sentiment information into sentiment transfer without parallel data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12036 LNCS, pp. 312–319). Springer. https://doi.org/10.1007/978-3-030-45442-5_39
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