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Generative Sentiment Transfer via Adaptive Masking

arXiv 2302.12045 ↗First posted 23 Feb 2023cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 21 of its 27 references that are in the map. First posted on arXiv 23 Feb 2023.

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21 of 27 references.

References · 21

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  11. 2018Unsupervised Text Style Transfer using Language Models as Discriminatorscited by 70
  12. 2018Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approachcited by 55
9 more
  1. 2019"Mask and Infill" : Applying Masked Language Model to Sentiment Transfercited by 48
  2. 2018Adversarial Text Generation via Feature-Mover's Distancecited by 27
  3. 2020Stable Style Transformer: Delete and Generate Approach with Encoder-Decoder for Text Style Transfercited by 22
  4. 2019STaDA: Style Transfer as Data Augmentationcited by 21
  5. 2019Semi-supervised Text Style Transfer: Cross Projection in Latent Spacecited by 21
  6. 2021Few-shot Controllable Style Transfer for Low-Resource Multilingual Settingscited by 20
  7. 2022Semi-Supervised Formality Style Transfer with Consistency Trainingcited by 12
  8. 2018Learning Sentiment Memories for Sentiment Modification without Parallel Datacited by 10
  9. 2020Understanding Pre-trained BERT for Aspect-based Sentiment Analysiscited by 9

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