SlowGuess/NAACL_2021_OCR huggingface dataset:
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 refinements 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	BARTD+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
BARTW/0EN	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	BARTD+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
BARTW/0EN	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.	3.7	4.0	2.3
Table 6: Examples of generated outputs from different systems (with human reframed argument as references) for arguments containing partisan collocations and appeal to fear, respectively. We show average scores (over three annotators) on a 1-5 scale with 1 denotes the worst and 5 be the best. More examples in Appendix
humans scores slightly better on meaning comparing with our system ENTRUST). 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. The BART $_{D+EN}$  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, EN-TRUST refinements are not static. Table 8 shows that for the same collocation of targeted killing, the refinements are different, contingent on the context. This goes on to prove that our model not only
1https://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.
An Iranian government official seemed to .... suggest that President Trump's properties .... in retaliation for the US involvement in the execution of Iranian ....
A federal appeals court ordered the U.S. Department of Justice to turn over key portions of a memorandum justifying the government's targeted killing of people linked to terrorism, including Americans
A federal appeals court ordered the U.S. Department of Justice to turn .... justifying the government's extrajudicial execution of people ... terrorism, ....
Table 7: Different reframed arguments by ENTRUST model based on same collocation
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 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 (Musi, 2018), where the goal is for the speaker to portray some point of agreement in a positive light before disagreeing.
10 Ethics
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.
Yamen Ajjour, Milad Alshomary, Henning Wachsmuth, and Benno Stein. 2019. Modeling frames in argumentation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2922-2932, Hong Kong, China. Association for Computational Linguistics.
Emily Allaway and Kathleen McKeown. 2020. A unified feature representation for lexical connotations.
Aristotle and R. Bartlett. 2019. Aristotle's "art of rhetoric".
Tal August, Nigini Oliveira, Chenhao Tan, Noah A. Smith, and Katharina Reinecke. 2018. Framing effects: Choice of slogans used to advertise online experiments can boost recruitment and lead to sample biases. *PACMHCI*, 2(CSCW):22:1-22:19.
Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning. 2015. A large annotated corpus for learning natural language inference. arXiv preprint arXiv:1508.05326.
Amparo Elizabeth Cano-Basave and Yulan He. 2016. A study of the impact of persuasive argumentation in political debates. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1405-1413, San Diego, California. Association for Computational Linguistics.
Tuhin Chakrabarty, Christopher Hidey, and Kathleen McKeown. 2019a. Imho fine-tuning improves claim detection. arXiv preprint arXiv:1905.07000.
Tuhin Chakrabarty, Christopher Hidey, Smaranda Muresan, Kathleen McKeown, and Alyssa Hwang. 2019b. Ampersand: Argument mining for persuasive online discussions. In Proceedings of the
2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2926-2936.
Dennis Lee Chong and James N. Druckman. 2007. Framing theory.
Giovanni Da San Martino, Seunghak Yu, Alberto Barrón-Cedeno, Rostislav Petrov, and Preslav Nakov. 2019. Fine-grained analysis of propaganda in news article. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5640-5650.
Dorottya Demszky, Nikhil Garg, Rob Voigt, James Zou, Matthew Gentzkow, Jesse Shapiro, and Dan Jurafsky. 2019. Analyzing polarization in social media: Method and application to tweets on 21 mass shootings. arXiv preprint arXiv:1904.01596.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
Tao Ding and Shimei Pan. 2016. Personalized emphasis framing for persuasive message generation. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1432-1441, Austin, Texas. Association for Computational Linguistics.
R. Entman. 1983. Framing: Toward clarification of a fractured paradigm. Journal of Communications.
Angela Fan, Mike Lewis, and Yann Dauphin. 2018. Hierarchical neural story generation. arXiv preprint arXiv:1805.04833.
Anjalie Field, Doron Kliger, Shuly Wintner, Jennifer Pan, Dan Jurafsky, and Yulia Tsvetkov. 2018. Framing and agenda-setting in russian news: a computational analysis of intricate political strategies. arXiv preprint arXiv:1808.09386.
G. Frege. 1892. Über sinn und bedeutung. Zeitschrift für Philosophie und philosophische Kritik, 100:25-50.
Matthew Gentzkow, Jesse M Shapiro, and Matt Taddy. 2019. Measuring group differences in high-dimensional choices: method and application to congressional speech. *Econometrica*, 87(4):1307-1340.
Sayan Ghosh, Mathieu Chollet, Eugene Laksana, Louis-Philippe Morency, and Stefan Scherer. 2017. Affect-LM: A neural language model for customizable affective text generation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 634-642, Vancouver, Canada. Association for Computational Linguistics.
Stephan Greene and Philip Resnik. 2009. More than words: Syntactic packaging and implicit sentiment. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 503-511, Boulder, Colorado. Association for Computational Linguistics.
Frederik Hartmann. 2019. Predicting historical phonetic features using deep neural networks: A case study of the phonetic system of Proto-Indo-European. In Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change, pages 98-108, Florence, Italy. Association for Computational Linguistics.
Mareike Hartmann, Tallulah Jansen, Isabelle Augenstein, and Anders Søgaard. 2019. Issue framing in online discussion fora. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1401-1407, Minneapolis, Minnesota. Association for Computational Linguistics.
Christopher Hidey, Elena Musi, Alyssa Hwang, Smaranda Muresan, and Kathleen McKeown. 2017. Analyzing the semantic types of claims and premises in an online persuasive forum. In Proceedings of the 4th Workshop on Argument Mining, pages 11-21.
Ari Holtzman, Jan Buys, Maxwell Forbes, Antoine Bosselut, David Golub, and Yejin Choi. 2018. Learning to write with cooperative discriminators. arXiv preprint arXiv:1805.06087.
Nitish Shirish Keskar, Bryan McCann, Lav R Varshney, Caiming Xiong, and Richard Socher. 2019. Ctrl: A conditional transformer language model for controllable generation. arXiv preprint arXiv:1909.05858.
Ashiqur R KhudaBukhsh, Rupak Sarkar, Mark S Kamlet, and Tom M Mitchell. 2020. We don't speak the same language: Interpreting polarization through machine translation. arXiv preprint arXiv:2010.02339.
Johannes Kiesel, Maria Mestre, Rishabh Shukla, Emmanuel Vincent, Payam Adineh, David Corney, Benno Stein, and Martin Potthast. 2019. SemEval 2019 task 4: Hyperpartisan news detection. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 829-839, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461.
Juncen Li, Robin Jia, He He, and Percy Liang. 2018a. Delete, retrieve, generate: A simple approach
to sentiment and style transfer. arXiv preprint arXiv:1804.06437.
Juncen Li, Robin Jia, He He, and Percy Liang. 2018b. Delete, retrieve, generate: a simple approach to sentiment and style transfer. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1865-1874, New Orleans, Louisiana. Association for Computational Linguistics.
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.
Xinyao Ma, Maarten Sap, Hannah Rashkin, and Yejin Choi. 2020. Powertransformer: Unsupervised controllable revision for biased language correction. arXiv preprint arXiv:2010.13816.
M. Mccombs and Salma I. Ghanem. 2001. The convergence of agenda setting and framing.
Saif M Mohammad and Peter D Turney. 2013. Crowdsourcing a word-emotion association lexicon. Computational Intelligence, 29(3):436-465.
Elena Musi. 2018. How did you change my view? a corpus-based study of concessions' argumentative role. Discourse Studies, 20:270 - 288.
Elena Musi and Mark Aakhus. 2019. Framing framing: Semantic frames as meta-argumentative indicators for knowledge-driven argument mining of controversies.
Elena Musi, Debanjan Ghosh, and Smaranda Muresan. 2018. Changemyview through concessions: Do concessions increase persuasion? arXiv preprint arXiv:1806.03223.
Ana Laura Nettel and Georges Roque. 2012. Persuasive argumentation versus manipulation. Argumentation, 26(1):55-69.
Viet-An Nguyen. 2013. Lexical and hierarchical topic regression. In NIPS 2013.
Jekaterina Novikova, Ondrej Dusek, Amanda Cercas Curry, and Verena Rieser. 2017. Why we need new evaluation metrics for NLG. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2241-2252, Copenhagen, Denmark. Association for Computational Linguistics.
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019. fairseq: A fast, extensible toolkit for sequence modeling. arXiv preprint arXiv:1904.01038.