Computer Science > Computation and Language
arXiv:2302.12045 (cs)
[Submitted on 23 Feb 2023]
Title:Generative Sentiment Transfer via Adaptive Masking
View a PDF of the paper titled Generative Sentiment Transfer via Adaptive Masking, by Yingze Xie and 5 other authors
View PDFAbstract:Sentiment transfer aims at revising the input text to satisfy a given sentiment polarity while retaining the original semantic content. The nucleus of sentiment transfer lies in precisely separating the sentiment information from the content information. Existing explicit approaches generally identify and mask sentiment tokens simply based on prior linguistic knowledge and manually-defined rules, leading to low generality and undesirable transfer performance. In this paper, we view the positions to be masked as the learnable parameters, and further propose a novel AM-ST model to learn adaptive task-relevant masks based on the attention mechanism. Moreover, a sentiment-aware masked language model is further proposed to fill in the blanks in the masked positions by incorporating both context and sentiment polarity to capture the multi-grained semantics comprehensively. AM-ST is thoroughly evaluated on two popular datasets, and the experimental results demonstrate the superiority of our proposal.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2302.12045 [cs.CL] |
| (orarXiv:2302.12045v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2302.12045 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Generative Sentiment Transfer via Adaptive Masking, by Yingze Xie and 5 other authors
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