Inducing Positive Perspectives with Text Reframing
Caleb Ziems, Minzhi Li, Anthony Zhang, Diyi Yang
Abstract
Sentiment transfer is one popular example of a text style transfer task, where the goal is to reverse the sentiment polarity of a text. With a sentiment reversal comes also a reversal in meaning. We introduce a different but related task called positive reframing in which we neutralize a negative point of view and generate a more positive perspective for the author without contradicting the original meaning. Our insistence on meaning preservation makes positive reframing a challenging and semantically rich task. To facilitate rapid progress, we introduce a large-scale benchmark, Positive Psychology Frames, with 8,349 sentence pairs and 12,755 structured annotations to explain positive reframing in terms of six theoretically-motivated reframing strategies. Then we evaluate a set of state-of-the-art text style transfer models, and conclude by discussing key challenges and directions for future work.
BibTeX
@inproceedings{ziems-etal-2022-inducing,
title = "Inducing Positive Perspectives with Text Reframing",
author = "Ziems, Caleb and
Li, Minzhi and
Zhang, Anthony and
Yang, Diyi",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.257/",
doi = "10.18653/v1/2022.acl-long.257",
pages = "3682--3700"
}