ENTRUST: Argument Reframing with Language Models and Entailment
Tuhin Chakrabarty, Christopher Hidey, Smaranda Muresan
Abstract
Framing involves the positive or negative presentation of an argument or issue depending on the audience and goal of the speaker. Differences in lexical framing, the focus of our work, can have large effects on peoples’ opinions and beliefs. To make progress towards reframing arguments for positive effects, we create a dataset and method for this task. We use a lexical resource for “connotations” to create a parallel corpus and propose a method for argument reframing that combines controllable text generation (positive connotation) with a post-decoding entailment component (same denotation). Our results show that our method is effective compared to strong baselines along the dimensions of fluency, meaning, and trustworthiness/reduction of fear.
BibTeX
@inproceedings{chakrabarty-etal-2021-entrust,
title = "{ENTRUST}: Argument Reframing with Language Models and Entailment",
author = "Chakrabarty, Tuhin and
Hidey, Christopher and
Muresan, Smaranda",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.394/",
doi = "10.18653/v1/2021.naacl-main.394",
pages = "4958--4971"
}