SlowGuess/NAACL_2021_OCR huggingface dataset:
Tuhin Chakrabarty<sup>1</sup>, Christopher Hidey <sup>3</sup> Smaranda Muresan<sup>1,2</sup>
Data Science Institute, Columbia University 2
Google  $^{3}$
{tuhin.chakr, smara}@cs.columbia.edu
chris.hide@gmail.com
Framing involves the positive or negative presentation of an argument or issue depending on the audience and goal of the speaker (Entman, 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 postdecoding 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.
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 ("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...
Rf Arg1	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.
Rf Arg3	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
Rf Arg4	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 (Mcombs 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).
Connotations may be distinguished along the
*The work is not affiliated to Google and was conducted independently outside of the organization
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4958-4971 June 6-11, 2021. ©2021 Association for Computational Linguistics
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: Argument Reframing with langUage modelS and enTailment, with the following contributions: 1) A Connotation-guided Masked Language Model approach to generate a parallel dataset of naturally occurring arguments and their refinings (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 subreddit, 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.
Connotation-guided Masked Language Model. 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
Figure 1: A schematic illustration of our system ENTRUST, where the top block shows our training process where we use MLM along with connotation resource to transform an original argument to an argument bearing different emotional connotation and use them to fine-tune BART. The block below shows the inference step (test time) where we use fine-tuned BART to reframe the argument containing partisan collocation along with a NLI component to ensure the same denotation with the input argument.
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 sepa
rating 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 auto-regressive 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 muslim 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).
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 refinements.
Bart Without Demarcator and ENtailMENT  $(\mathbf{B A R T}_{D + E N}^{w / o})$  : 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  $(\mathbf{BART}_{EN}^{w/o})$ : 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
BARTD+ENw/o	64.1	38.5
BARTDw/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
BARTD+ENw/o	2.78	2.56	2.60	3.01
BARTENw/o	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 grammat
ical 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 reflecting 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, Trust-Wordiness and Presence of Fear is 0.62, 0.65, 0.51, 0.46, respectively.