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. To download the data, see https://github.com/GT-SALT/positive-frames.
Cite
CITATION STYLE
Ziems, C., Li, M., Zhang, A., & Yang, D. (2022). Inducing Positive Perspectives with Text Reframing. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (Vol. 1, pp. 3682–3700). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.acl-long.257
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