@inproceedings{6886135f67784569bafcce0ea3f3998a,
title = "RAST: A Reward Augmented Model for Fine-Grained Sentiment Transfer",
abstract = "In this paper, we propose a novel model RAST (Reward Augmented Sentiment Transfer) for fine-grained sentiment transfer. Existing methods usually suffer from two major drawbacks, i.e., blurre d sentiment distinction and unsatisfactory content preservation. Considering the above issues, we design two kinds of rewards to better control sentiment and content. Specially, we develop a pairwise comparative discriminator that enforces to generate sentences with clear distinctions for different sentiment intensities. Moreover, we utilize an effective sampling strategy to obtain pseudo-parallel sentences with minor changes on the input sentence to enhance content preservation. Experiments on a benchmark dataset show that the proposed model outperforms several competitive approaches.",
keywords = "Fine-grained sentiment transfer, Reward augmented training",
author = "Xiaoxuan Hu and Hengtong Zhang and Zhao, \{Wayne Xin\} and Yaliang Li and Jing Gao and Wen, \{Ji Rong\}",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 10th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2021 ; Conference date: 13-10-2021 Through 17-10-2021",
year = "2021",
doi = "10.1007/978-3-030-88480-2\_16",
language = "英语",
isbn = "9783030884796",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "196--209",
editor = "Lu Wang and Yansong Feng and Yu Hong and Ruifang He",
booktitle = "Natural Language Processing and Chinese Computing - 10th CCF International Conference, NLPCC 2021, Proceedings",
address = "德国",
}