Framing involves the positive or negative presentation of an argument or issue depending on the audience and goal of the speaker (Ent-man, 1983). 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 postde-coding entailment component (same denota-tion). Our results show that our method is effective compared to strong baselines along the dimensions of fluency, meaning, and trustwor-thiness/reduction of fear. 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 (Chong and Druckman, 2007). 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 (Rasinski, 1989). While other research has focused on syntactic framing (Greene and Resnik, 2009) or issue framing (Hartmann, 2019), we focus specifically on lexical framing, distinguishing sentences by their connotative meaning even where they have the same denotative meaning. According to Frege (1892), two sentences with the same truth conditions may refer to the same entities or state of affairs ( refer-ence, also known as denotation) but be presented  The work is not affiliated to Google and was conducted independently outside of the organization Arg1 Alabama's Supreme Court Chief Justice was suspended... for ordering state probate judges not to grant marriage licenses to gay couples... RfArg1 Alabama's Supreme Court Chief Justice was suspended... for ordering state probate judges not to grant legal marriage equality to gay couples... Arg2 Every nation with territorial claims in the arctic is a member of NATO, except Russia. Rf Arg2 Every nation with sovereign competence in the arctic is a member of NATO, except Russia. Arg3 At this dire moment , we all need to amplify our voices in defense of free speech . RfArg3 At this crucial moment , we all need to amplify our voices in support of free speech. Arg4 It is difficult to think of any single act that would do more to restore America's soft power than the election of Obama to the presidency RfArg4 It is difficult to think of any single act that would do more to restore America's diplomatic credibility than the election of Obama to the presidency Table 1: Examples of arguments (Arg1, Arg2) with high partisan skew collocations (in red) (Webson et al., 2020) as well as appeal to fear or prejudice argument fallacies (Arg3, Arg4) (Da San Martino et al., 2019), along with reframed arguments as an attempt by our model ENTRUST to improve trustworthiness. differently ( sense or connotation). For example, undocumented workers and illegal aliens have the same denotation but different connotations (Webson et al., 2020). The examples in Table 1 are instances of lexical framing, where word choice determines the difference in presentation (Mccombs and Ghanem, 2001). For example, Arg1 and Arg2 contain collocations (in red) that have a high partisan skew (Webson et al., 2020), while Arg 3 and Arg 4 are examples of appeal to fear or prejudice argument fallacies from propagandist news articles (Da San Martino et al., 2019). The goal is to reframe such arguments to be more trustworthy (e.g., less partisan, no appeal to fear fallacy). dimensions of politeness, sentiment, or tangibility, among others (Allaway and McKeown, 2020), but in our work we consider emotional association such as fear and trust. Appeal to fear is considered an argumentative fallacy (Walton, 2006; Thierer, 2012) and appears prominently in manipulative text such as propaganda (Da San Martino et al., 2019). On the other hand, arguments with trusted language align with the Aristotelian modes of persuasion, specifically ethos (Aristotle and Bartlett, 2019). In our work, we leverage such a lexical resource for connotations (Allaway and McKeown, 2020) to reframe arguments to be more trustworthy (e.g., less partisan, no appeal to fear fallacy), while maintaining the same denotative meaning. While retrieve-and-replace methods perform well on other attribute transfer tasks such as sentiment (Li et al., 2018a; Sudhakar et al., 2019a), our task is more dependent on broader context within a sentence even though we are performing localized replacement. Thus, there are two main challenges we need to address: 1) the lack of a parallel dataset of negatively and positively framed arguments (naturally-occurring); and 2) a generation approach that can not only change the connotative meaning but also keep the same denotative meaning of the input argument. We introduce our approach called ENTRUST : Argum ENT R eframing with lang U age model S and en T ailment, with the following contributions: 1) A Connotation-guided Masked Language Model approach to generate a parallel dataset of naturally oc-curing arguments and their reframings (Section 2); 2) A method for argument reframing that combines controllable text generation (connotative meaning associated with trust) and entailment (same denotative meaning) (Section 3); 3) An evaluation on two different tasks  reframing partisan arguments and appeal to fear/prejudice fallacies  showing that our method is preferred over a strong retrieval-based baseline (Sudhakar et al., 2019a) and state-of-the-art pretrained language model (Lewis et al., 2019), and it is close to human performance on several evaluation criteria such as fluency, meaning, trustworthiness/reduction in fear. Code, data, and models available at https://github.com/ tuhinjubcse/ArgReframingNAACL2021 2 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. Selection of naturally-occurring arguments. Since our goal is to re-write arguments, it is essential to identify an abundant source of naturally-occurring arguments. The Change My View sub-reddit, an argumentative discussion forum intended for persuasion on diverse topics, has been used extensively in computational argumentation research (Tan et al., 2016; Wei et al., 2016; Musi et al., 2018; Chakrabarty et al., 2019a,b; Hidey et al., 2017). We collect sentences from the same source and classify them as claim , premise , or non-argument using the fine-tuned BERT model released by Chakrabarty et al. (2019b). This results in 301,166 arguments labeled as premises. We consider only premises to create our parallel data because argumentative appeals occur within justifications (premises) for or against the speaker's claim. Allaway and McKeown (2020) provide a resource with words labeled for lexical connotations, using the aspects of Social Value, Politeness, Impact, Factuality, Sentiment, Emotional Association . For our work we only consider Emotional Association , although in future work our methods could be applied for other aspects. To create a parallel corpus, we use this lexical resource and the 301,166 automatically identified premises from Change My View to obtain candidate words within those premises for replacement. We match words from the premises to those that have entries in the dictionary with emotional connotations such as fear, trust, anticipation, and joy. To generate replacements for these words, we need to find substitutions that maintain denotative meaning while changing connotative meaning. We use the connotation dictionary to address the latter. However, to address the former, we need to provide only paraphrases that consider the context in which these words occur. We thus use a masked language model (MLM). Masked language modeling approaches like BERT (Devlin et al., 2018) and RoBERTa (Liu et al., 2019) can be considered cloze or fill-in-the-blank tasks, where the model uses the context surrounding a masked-out token to try to predict what the masked word should be. We borrow this framework (RoBERTa-large, in particular) to mask the candidate words we identified via the connotation lexicon. However, the rank of a predicted token from an MLM is based on the language model probability  it provides no information about lexical connotations. A premise re-written from MLM replacements may thus have the same connotative meaning. To avoid this scenario, we restrict the MLM replacements to be words with different connotations than the original masked word (i.e., different Emotional Association). Our data creation process is depicted in Figure 1. In example 2, the word resources has the connotations joy;trust in our dictionary. The MLM generates the replacement tools, which we verify has a different connotation ( emotionally neutral ). For example 1, the words "prepare," "real," and "defense" have the emotional connotations anticipation , trust , and anticipation;anger;fear respectively. These words are replaced with plan , your , and safety , using our MLM. We treat the original premises as the target and the connotation-guided MLM generated premises as the source" for our method of argument reframing detailed in the next section (Figure 1). While this process provides us with a parallel dataset for reframing, we enhance the source-side of the data to provide additional control during generation. Motivated by the work of Schiller et al. (2020), which used aspect as a control code (Keskar et al., 2019) for argument generation, we also prepend the emotional associations of the replaced words. Using the connotations from the lexical resource, we add all listed emotions as control codes by separating them with a special token ([DELIM]) (the top right block of Figure 1). During inference, we thus have more control over the emotion of the words we are generating (in our case we specifically use trust as the control code). For additional control, we also insert demarcator tokens ([SEP]) at the boundary of the words we aim to replace to provide our generative model with a better signal on what to replace or rewrite. While the downside is that we need to identify spans for replacement at test/inference time, our experiments will show that using collocations or fear words makes it unnecessary. By using the lexical connotation resource we do not have to rely on a separate module/tagger based approach like that of Pryzant et al. (2020) to find biased or problematic words that may introduce additional noise during training. Our parallel data has 271,022 pairs for training and 30,114 for validation on which perplexity is evaluated. 3 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 trust <V>I suppose we could argue that they're much better at soft power than Nazi Germany or the USSR, 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 ...... Table 2: Generation from fine-tuned BART without control for entailment can sometime contradict the input thereby failing to maintain the same denotative meaning 3.1 Controllable Text Generation BART (Lewis et al., 2019) is a pre-trained model combining bidirectional and auto-regressive transformers that achieves state-of-the-art results in several text generation tasks. It is implemented as a sequence-to-sequence model with a bidirectional encoder over corrupted text and a left-to-right autoregressive decoder. In principle, the pre-training procedure has two stages: (1) text is corrupted with an arbitrary noising function, and (2) a transformer-to-transformer model is learned to reconstruct the original text. Because BART has an auto-regressive decoder, it can be directly fine-tuned for most sequence generation tasks. Here, the encoder input is a sequence of words, and the decoder generates outputs auto-regressively. We refer the reader to (Lewis et al., 2019) for further details. For our task, we fine-tune BART on our parallel data, where the reframed argument using MLM & connotation dictionary is the encoder source and the original argument is the decoder target (Figure 1). The emotional connotations added to the source via the special token DELIM (see Section 2) act as a control code for generation. Moreover, for lexical framing, subtle differences in word choices matter the most. By explicitly using special tokens ([SEP]) in our parallel data during fine-tuning, the BART model learns what to edit, instead of editing random words in the sentence, a common issue often found in attribute transfer models (Li et al., 2018a; Sudhakar et al., 2019a). At test time, therefore, we can ensure the model reframes a desired content span. All hyper-parameters are mentioned in the Appendix A . Post fine-tuning at the decoding step, we use a top-k sampling strategy (Fan et al., 2018) to reframe arguments conditioned on a input argument and a target emotion. 3.2 Post-decoding NLI Our task is challenging in comparison to traditional text attribute transfer tasks as we need to maintain the same denotative meaning as the input. While in most cases BART is able to generate content which is semantically similar to the input, it sometimes contradicts the input. For example, Table 2 shows that BART changes soft power to military strength . Here the denotative meaning changes. To control for this, we introduce an additional post-processing step. We generate multiple outputs by varying the value of k (between 5 and 50) while conducting top-k sampling. We then calculate the entailment scores of these outputs with the input argument respectively using a RoBERTa (Liu et al., 2019) model fine-tuned on the Multi-NLI dataset (Williams et al., 2018) and then select the output having the best entailment score. We also experimented with other methods for incorporating entailment during decoding based on prior work (Section 8), but found these techniques to be less effective than our method. As pre-trained sequence-to-sequence language models are good at copying input and generating natural-sounding output, we hypothesize that our approach will better allow us to change connotative meaning without affecting fluency and denotation. In contrast, approaches such as vocab boosting (Ghosh et al., 2017) increase the logits of key connotative words, which would necessarily decrease the probabilities of functional words and words necessary for maintaining denotative meaning. Other approaches such as reinforcement learning (Pasunuru and Bansal, 2017) may further decrease these desired qualities, while trying to maximize another objective. 4 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 Webson et al. (2020), 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 (Gentzkow et al., 2019) 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 Table 3: INP1 and INP2 are test data instances where INP1 is a Appeal to Fear example while INP2 is an argument containing partisan collocation (Kiesel et al., 2019) that occur at least 100 times and have high partisan skew. We use these words to filter arguments from the subreddits ChangeMyView and Politics . Some of these collocations include phrases such as abortion providers, investment vehicles, broken system, soft power, and territorial claims . We randomly sample 100 such arguments to benchmark the performance of our model and further use towards human evaluation. In addition, we test our models on propaganda techniques employed in news articles with an Appeal to Fear or Prejudice (Da San Martino et al., 2019). There are a total of 182 sentence-level text fragments labeled as Appeal to Fear or Prejudice in the dataset released by Da San Martino et al. (2019). 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 Webson et al. (2020) appear in the connotation dictionary of Allaway and McKeown (2020), 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 Table 3), while for arguments with partisan collocation the human was instructed to change the collocation so as to make it trustworthy (HUM2 in Table 3). 5 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 (Webson et al., 2020), 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). 5.1 Baseline Systems As argument reframing is a new task, we adapt several baselines that have been used for other generation tasks and also compare with human-generated reframings. Bart wIthout Demarcator and ENtailment (BART w/o D + EN ): This is the pre-trained BART model fine-tuned on our parallel data without explicitly adding signals on what to edit or reframe and without post-processing based on entailment scores. This experiment helps us understand if BART learns to adapt to the emotional connotations and can automatically edit partisan collocations or words inducing fear without control. Bart without EnTAilment (BART w/oEN ): This is the pre-trained BART model fine-tuned on our parallel data with explicit signals ([SEP] token) but without the NLI component as a post-processing tool. This experiment helps us understand how well BART learns to adapt to the emotional connotations without altering the denotative meaning once guided with what to reframe. Lexical Replacement (LEXREP): We use a similar method employed for our parallel data creation. We rely on Masked Language Models for lexical substitutions. Because our goal is to reframe arguments to be trustworthy we prefer substitutions which have a connotation of trust in the resource by Allaway and McKeown (2020). In case we cannot find the substitution in the connotation dictionary we honor default MLM predicted infilling. Generative Style Transformer (GST): We use the state of art for text style transfer by Sudhakar et al. (2019a), which is a part of a larger  Delete Retrieve Generate " framework (Li et al., 2018a). To maintain parity with other baselines, instead of letting the model delete attribute keywords we delete System Partisan Task Appeal to Fear Task BART w/oD + EN 64.1 38.5 BART w/oEN 91.9 43.1 GST 86.4 38.3 LEXREP 92.4 44.3 ENTRUST 92.9 44.5 HUMAN 93.9 41.6 * Table 4: Semantic Similarity of reframed arguments with input arguments. (*) Here human did not restrict themselves to just lexical framing, so automated metrics might penalize them for more reframing. System Fluency Meaning Trust  Fear  INPUT -3.24 3.36 BART w/oD + EN 2.78 2.56 2.60 3.01 BART w/oEN 3.39 3.00 3.13 2.58 LEXREP 3.38 3.00 3.08 2.54 GST 2.14 1.81 2.01 2.44 ENTRUST 3.51 3.30 3.52 2.39 HUMAN 3.72 3.63 3.71 2.59 Table 5: Fluency and Meaning Preservation scores given by human judges on a scale of (1-5) for reframed arguments with respect to input arguments. Fluency and Meaning Preservation ratings are for all arguments in test set, while Trust ratings are for arguments with Partisan collocation (higher scores better), and Fear ratings for Appeal To Fear or Prejudice ones only (lower scores better). the partisan collocations or fear related words from the arguments as the first step, followed by the usual retrieve and generate steps. Our training data for this method includes only arguments labeled with their attribute (e.g., positive or negative). Arguments containing lexical connotations catering to trust are positive, while those not catering to trust are negative. 5.2 Evaluation Criteria Automatic evaluation. One important criterion is to measure if the reframed arguments are faithful to the input. Even though we are changing the argument for connotations, it should still maintain the same denotative meaning as the input. To this end we calculate Semantic Similarity with our input using SENTENCE BERT(SBERT) (Reimers and Gurevych, 2019). Human evaluation. We use Amazon Mechanical Turk to evaluate on a total of 900 utterances, 750 generated from 5 systems and 150 utterances generated by humans. We proposed a set of 3 criteria to evaluate the generated output: (1) Fluency (F) (How fluent and grammatical are the utterances?), (2) Meaning Preservation (M) (How well does the reframed argument capture the same denotative meaning as the input argument?), (3) Trustworthiness/Presence of Fear(T/PF) . For the 100 input arguments reflect-ing partisan view we ask Turkers to rate reframed arguments based on trustworthiness with respect to the input. For the 50 Appeal to Fear or Prejudice fallacies we ask Turkers to rate reframed arguments based on presence of fear (the intention behind this being that we want to rank systems which portray the least amount of fear). In both of these ratings we still ask Turkers to keep into account the denotative meaning (i.e., making it trustworthy or less fallacious at the expense of meaning alterations should be scored lower). We hired 40, 25, 39 (23 and 16) Turkers for the three separate tasks respectively. The computed IAA using Krippendorff's alpha for Fluency, Meaning Preservation , TrustWorthiness and Presence of Fear is 0.62, 0.65, 0.51, 0.46, respectively. Automatic Evaluation. As can be seen in Table 4 our model ENTRUST maintains the denotative meaning with the input better than other systems ( p < . 001 using approximate randomization tests) and only marginally behind humans when it comes to arguments with partisan collocations. For Appeal to Fear or Prejudice our system maintains better denotative meaning than all systems except LEXREP ( p < . 001 ). The automatic metric somewhat penalizes humans for changing more content than just targeted words; this unreliability is a known issue with automated metrics (Novikova et al., 2017) and strongly implies a need for human evaluation. Human Evaluation. Table 5 shows the results of our human-based evaluations. For fluency, meaning preservation, trustworthiness, and reduction of fear the ENTRUST model is better than all the baselines ( p < . 001 using approximate randomization tests). It is further encouraging to see that the entailment step helps us maintain better denotative meaning (See Table 5 Col3: Row 4 vs Row 7). For Presence of Fear, Turkers often rate our ENTRUST model to be the least fearful, including slightly when compared to reframings of an expert. We hypothesize this is because the human judges found it difficult to completely remove fear while keeping the denotative meaning (indeed the Original Argument System reframed Argument F M T  /PF  It is difficult to think of any single act that would do more to restore America's soft power than the election of Obama to the presidency BART w/oD + EN It is difficult to think of any single act that would do more to restore America's soft power than the election of Obama to the presidency 3.7 2.3 3.3 BART w/oEN It is difficult to think of any single act that would do more to restore America's moral authority than the election of Obama to the presidency 3.7 3.3 3.0 LEXREP It is difficult to think of any single act that would do more to restore America's moral standing than the election of Obama to the presidency 3.7 3.3 2.7 GST Hated it is difficult to think of any single act that would do more to restore America's economy than the election of Obama to the presidency 1.7 1.7 2.7 ENTRUST It is difficult to think of any single act that would do more to restore America's diplomatic credibility than the election of Obama to the presidency 4.3 3.7 5.0 HUMAN It is difficult to think of any single act that would do more to restore America's political and economic influence than the election of Obama to the presidency 4.0 3.3 4.0 Or he can seize this opportunity and use his government's diplomatic influence to defend the life of an australian citizen , whose courageous public service is recognised by countless people across the world BART w/oD + EN Or he can seize this opportunity and use his governments's diplomatic influence to defend the life of an australian citizen , whose courageous military service is recognised by countless people across the world 2.7 3.0 2.3 BART w/oEN Or he can honor this opportunity and use his nation's diplomatic resources to honor the life of an australian citizen , whose unwavering public service is recognised by countless people across the world 4.0 4.7 1.3 LEXREP Or he can take this opportunity and use his nations's diplomatic resources to defend the life of an australian citizen , whose courageous military service is recognised by countless people across the world 4.7 4.0 2.0 GST Or he can do this opportunity and use his diplomatic expertise to change the life of an australian citizen , whose public service is recognised by countless people across the world 1.7 2.0 2.0 ENTRUST Or he can honor this opportunity and use his nation's diplomatic resources to vindicate the life of an australian citizen , whose unwavering public service is recognised by countless people across the world 4.7 4.7 1.3 HUMAN Or he can pick up this opportunity and use his government's diplomatic influence to defend the life of an Australian citizen, whose actions have been publicly recognized as highly relevant at an international level . Sometimes, an ungrammatical generation or a reframing which change the meaning will contain less fear (rating 1 meaning no fear at all). However, to avoid this we explicitly asked Turkers to rate those samples as moderate so as to not bias the overall results. As can be seen in Table 6, the ENTRUST model accurately captures diplomatic credibility as an alternate for soft power which is encouraging as soft power is measured through culture, diplomacy, education, business/innovation, and government. 1 The BART w/o D + EN model often fails to reframe anything, which shows the importance of adding [SEP] tokens as explicit supervision so that the model knows what to edit. The GST model fails at both grammaticality and meaning preservation, which makes it harder to judge its trustworthiness and ability to ameliorate fearful appeal. Finally, ENTRUST reframings are not static. Table 8 shows that for the same collocation of targeted killing , the reframings are different, contingent on the context. This goes on to prove that our model not only 1 https://en.wikipedia.org/wiki/Soft_ power An Iranian government official seemed to suggest that President Trump's properties could be potential targets in retaliation for the US targeted killing of Iranian general Qassem Soleimani. generalizes to unseen test data, but can produce novel, grammatical and meaningful edits based on context. 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). Demszky et al. (2019) and Tyagi et al. (2020) 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 Pryzant et al. (2020), 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. Greene and Resnik (2009) studied syntactic framing, finding a link between implicit sentiment and syntactic packagings. Previous studies have also involved emphasis framing  Ding and Pan (2016) 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). Nguyen (2013) and Field et al. (2018) study agenda-setting for news and congressional debates and August et al. (2018) for study recruitment. Cano-Basave and He (2016) and Musi and Aakhus (2019) 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 (Li et al., 2018b; Sudhakar et al., 2019b), 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. Holtzman et al. (2018) utilize a discriminative model trained on SNLI (Bowman et al., 2015) 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 Holtzman et al. (2018) with MNLI to account for entailment in text generation, none of them yielded better results than our method. Other approaches have explored vocab boosting (Ghosh et al., 2017) for tasks such as de-biasing (Ma et al., 2020), 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. 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 language 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 (Musi, 2018), where the goal is for the speaker to portray some point of agreement in a positive light before disagreeing. Our data is collected from Reddit and we understand and respect user privacy. Our models are fine-tuned on sentence level data obtained from user posts. These do not contain any explicit detail which leaks information about a users name, health, negative financial status, racial or ethnic origin, religious or philosophical affiliation or beliefs, sexual orientation, trade union membership, alleged or actual commission of crime. Second, although we use language models trained on data collected from the Web, which have been shown to have issues with bias and abusive language (Sheng et al., 2019; Wallace et al., 2019), the inductive bias of our models should limit inadvertent negative impacts. Unlike model variants such as GPT, BART is a conditional language model, which provides more control of the generated output. We have two levels of control on our generation approach: lexical replacements via connotations associated with trust and an entailment method that aims to keep the same denotation of the original argument. While dual-use concerns are certainly possible here, we think that open-sourcing this technology will help to generate arguments with more balanced and trusted language that are less targeted towards partisanship or appeals to fear. Finally, while there may be concerns about building generative models for persuasion, social scientists distinguish persuasion from manipulation based on two aspects: dissimulation and constraint (Nettel and Roque, 2012). Dissimulation involves concealing intention, which requires hiding information, whereas constraint involves removing options from the audience and forcing them to accept the conclusion. Our work on reframing arguments does not aim to hide information about a topic or present it as the only choice, but aims to provide the same argument using more balanced and trusted language. We achieve this by two key components of our technology: controllable text generation (connotation associated with trust) and entailment model to ensure same denotation. The technology should be used responsibly, particularly making sure the generation is controllable for trust and positive emotion and that the entailment component is used for ensuring the same denotation with the original argument. Finally we pay the Turkers at a rate of 15$/hour, complying with minimum wage standards in most places.
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