Abstract
Text sentiment transfer models modify sentence sentiments while retaining their semantic content. The main challenge is to separate sentiment-independent content information from the semantic information of the sentence. The previous works usually expect to utilize the model encoder to infer the sentence-level representation that removed sentiment information. However, the strength of the models’ abilities to reconstruct text is difficult to control which resulting encoder infer sentence-level sentiment-independent content embedding failed. In this paper, we address this challenge by using word-level representation. We first use the POS-Tagging technique to tag the part of speech of word sequence, then extracting content keywords by three schemes and use them as input, rather than the entire sentence, to obtain a purer word-level sentiment-independent content representation. In this way, the model does not require to infer the sentiment-independent representation, which avoids the instability of the adversarial training process. Experiments show that our method achieves the state-of-the-art performance and is also effective in long text sentiment transfer tasks.
Supported by China National Social Science Fund (19BXW110).
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Hu, S., Li, B., Lin, K., Wang, R., Liu, K. (2020). Text Sentiment Transfer Methods by Using Sentence Keywords. In: Fujita, H., Fournier-Viger, P., Ali, M., Sasaki, J. (eds) Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices. IEA/AIE 2020. Lecture Notes in Computer Science(), vol 12144. Springer, Cham. https://doi.org/10.1007/978-3-030-55789-8_4
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