ENTRUST : Argument Reframing with Language Models and Entailment (2021)

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.

## introduction public opinion has been shown to be significantly influenced by framing effects. framing refers to the presentation of an issue, where even small changes may have outsized effects on beliefs @xcite . for example, when asked about "welfare," the american public is largely against increasing spending (with only 20% in favor), but when asked about "assistance to the poor," 65% believe that the government is not spending enough @xcite . while other research has focused on syntactic framing @xcite or issue framing @xcite , we focus specifically on lexical framing, distinguishing sentences by their connotative meaning even where they have the same denotative meaning. according to @xcite , two sentences with the same truth conditions may refer to the same entities or state of affairs ("reference," also known as denotation) but be presented arg1 alabama's supreme court chief justice was suspended... for ordering state probate judges not to grant marriage licenses to gay couples... ## automatic parallel data creation to facilitate the reframing of arguments, we require a large-scale parallel corpora of sentences with the same denotation but different connotative meaning. ## method for argument reframing as our goal is to change connotation while maintaining denotation, we divide our approach to rewriting arguments into two primary tasks: 1) generating the appropriate lexical substitutions while being pertinent to the context; 2) ensuring that rewritten arguments reflect the desired emotional association while maintaining the same denotative meaning as the input. source @xmath0, but come on bart i suppose we could argue that they're much better at military strength than .......... bart + nli i suppose we could argue that they're much better at diplomatic communication than ...... ## evaluation tasks and test data to evaluate our methods for argument reframing we need to look beyond our automatically labeled data. we consider two tasks: 1) reframing an argument that contains partisan language to a less partisan argument; and 2) reframing an appeal to fear or prejudice fallacy to an argument without this fallacy. recently @xcite , proposed resources and methods to disentangle denotation and connotation in vector spaces. they evaluate their methods on a sample of around 300 collocations from vocabulary of congressional records @xcite and hyper-partisan news inp1 it would be dangerous , suicidal folly for infidels to pretend that ramadan is not the month of jihad hum1 it would be counterproductive and unreasonable for infidels to ........ jihad inp2 trump backs away from further military confrontation with iran hum2 trump backs away from further military engagement with iran in addition, we test our models on propaganda techniques employed in news articles with an appeal to fear or prejudice @xcite . there are a total of 182 sentence-level text fragments labeled as appeal to fear or prejudice in the dataset released by @xcite . we classify these 182 fragments as claims/premises/non-argument and randomly sample 50 premises. our goal is to reduce the fallacious nature of the argument without changing the denotative meaning. as our training distribution is different from these two datasets, these tasks and test sets allow us to better test the generalization capabilities of our models. furthermore, almost none of the collocations introduced by @xcite appear in the connotation dictionary of @xcite , which helps us avoid the risk of mimicking replacements from our training data. for both of these tasks, we ask humans to generate reframings based on our input test data for comparison and benchmark. we recruit two experts with argumentation and journalism background (not authors of this paper) to reframe arguments. for appeal to fear the instructions given were to make it less fallacious by reducing the fear and rephrasing the argument (hum1 in ## experimental setup to compare the quality of the reframed arguments, we benchmark our entrust model against human performance and four baseline systems described below. for the data containing collocations from @xcite , because we know they represent partisan language the ideal goal is to reframe them. for appeal to fear or prejudice data we reframe words which portray an emotion of fear based on the popular nrc emotion lexicon (mohammad and turney, 2013). ## analysis and discussion as can be seen in ## related work the effects of lexical framing have been studied for social and political issues, although our work is the first to use lexical framing in generation for positive framing effects (less partisan, no appeal to fear fallacy). @xcite and @xcite study political polarization and how this manifests in differences in word choice among different groups; khudabukhsh et al. (2020) provide an interpretable framework using machine translation between groups to generate differences. while these works encourage computational approaches to reframe arguments for better lexical choice, these approaches do not control for denotation or connotation and thus may cause differences in word choice to result in a change in meaning. the most similar work to ours is that of @xcite , who use a corpus of wikipedia edits to train a model for debiasing, which includes framing. however, in their work communicative intent is left implicit; the corpus is only labeled for types of debiasing, which includes framing at a high level and not the connotations involved. thus, their model only learns lexical differences, whereas our model is controllable. while our focus is on lexical framing, other work has investigated the identification of other types of frames and their effects. @xcite studied syntactic framing, finding a link between implicit sentiment and syntactic packagings. previous studies have also involved emphasis framing - @xcite find that emphasizing aspects of products given personal information is more effective for content selection in advertisements. other research has involved issue framing -ajjour et al. (2019) and hartmann et al. (2019 ) study how arguments are framed in debates (e.g., in terms of economics or safety). @xcite and @xcite study "agenda-setting" for news and congressional debates and @xcite for study recruitment. cano-basave and he (2016) and @xcite leverage semantic frames for distant labeling and analysis of arguments in political debates, respectively, and find, for example, that evidence and reasoning are among the most common. however, these approaches have focused on identification rather than generation. finally, our work is also related to style transfer and controllable generation. much of the work in "style transfer" has referred to changing the sentiment of a statement, which changes the truth condition and thus the denotative meaning. sentiment is often explicitly marked and thus approaches such as deleting and replacing lexical markers are effective @xcite , although our experiments showed the difficulty of applying these techniques to our task. to control text generation by limiting contradictions, pasunuru and bansal (2017) use an entailment score as a reward in reinforcement learning, ensuring that a generated text is logically implied by the ground-truth text. @xcite utilize a discriminative model trained on snli @xcite to complement an rnn generator and guide the decoding process to improve contradictions in generation. although we experimented with both of these approaches, including the approach of @xcite with mnli to account for entailment in text generation, none of them yielded better results than our method. other approaches have explored "vocab boosting" @xcite for tasks such as de-biasing @xcite , which involves increasing the values of certain words; however, as these values are on the simplex, the softmax function necessarily decreases the values of other logits which are key to fluency such as function words. ## conclusion our experiments showed that our approach is effective in reframing partisan arguments and appeals to fear for increased trustworthiness. we provided a method for creating a dataset using a lexical resource for connotations and masked lan-guage modeling. we used this dataset to fine-tune a controllable text generation model for the task of changing connotative meaning and used a model trained for natural language inference to maintain the denotative meaning. our evaluations found that our approach generalized to two different tasks and data sets. in future work, we plan to directly incorporate the role of stance in framing (for arguments and counter-arguments). we also plan to expand our work to generating concessions @xcite , where the goal is for the speaker to portray some point of agreement in a positive light before disagreeing.