Inducing Positive Perspectives with Text Reframing (2022)

Abstract: Sentiment transfer is one popular example of a text style transfer task, where the goal is to reverse the sentiment polarity of a text. With a sentiment reversal comes also a reversal in meaning. We introduce a different but related task called positive reframing in which we neutralize a negative point of view and generate a more positive perspective for the author without contradicting the original meaning. Our insistence on meaning preservation makes positive reframing a challenging and semantically rich task. To facilitate rapid progress, we introduce a large-scale benchmark, Positive Psychology Frames, with 8,349 sentence pairs and 12,755 structured annotations to explain positive reframing in terms of six theoretically-motivated reframing strategies. Then we evaluate a set of state-of-the-art text style transfer models, and conclude by discussing key challenges and directions for future work.

## introduction gratitude is not only the greatest of virtues, but the parent of all the others. in this work, we introduce a closely related taskpositive reframing-that differs from sentiment tst in important ways. we effectively reframe negative text by inducing a complementary positive viewpoint (e.g. glass-half-full), which nevertheless supports the underlying content of the original sentence. the reframe should implicate rather than contradict the source (see figure 1 ), and the transformation should be motivated by theoretically justified strategies from from positive psychology @xcite ; see section 3). to use the example from before, we could reframe "this was a bland dish" with the self-affirmation strategy and say "i've made dishes that are much tastier than this one." this reframed one still communicates the author's original intention by conversationally implicating that the dish was unsatisfying @xcite , but it shifts the focus away from the negative judgment and onto a positive and self-affirming perspective. numerous studies have shown the positive effects of this and other reframing strategies on well-being and cognitive performance @xcite @xcite , which motivate this work. our main contribution is the design and implementation of a new positive reframing task. to facilitate research in this space, we introduce a parallel corpus of 8,349 reframed sentence pairs and 12,755 structured annotations for six theoreticallymotivated re-write strategies. this is a significant contribution, especially since rich parallel corpora are scarce in tst tasks. some related datasets exist for politeness @xcite and sentiment transfer @xcite ), but they lack this parallel structure. with only unaligned corpora, researchers are limited to unsupervised training paradigms, which notoriously fail to disentangle style from content, and thus also fail to preserve meaning @xcite . using our parallel corpus, we examine how current state-ofthe-art neural models work for positive reframing. we find that, supervised transformer-based neural models appear capable of rewriting a negative text without contradicting the original premise of that text. however, these models still struggle to generate reasonable positive perspectives, suggesting that our dataset will serve as a useful benchmark for understanding psychologically well-motivated strategies for augmenting text with positive perspectives. ## positive reframing framework in this section, we present our psychologicallymotivated taxonomy of positive reframing strategies. instead of merely swapping antonyms for negative words or inserting unfounded positive language into a sentence, these strategies work to more fundamentally reconstruct the author's fixed, global, and ultimately harmful selfnarratives, which are known in the literature as cognitive distortions @xcite @xcite . cognitive distortions include many exaggerated or irrational self-focused thoughts @xcite , such as dichotomous "all-or-nothing" thinking @xcite , over-generalization @xcite , and catastrophizing @xcite . we can reconstruct these ideas using strategies from positive psychology @xcite . each strategy is designed to promote a beneficial shift in perspective without distorting the underlying context of the author's situation. growth mindset or, alternatively, the incremental theory of personality @xcite , is the belief that one's skills and abilities are not immutable but can instead be changed and improved over time @xcite ; that one's willpower is an abundant rather than limited or exhaustible resource @xcite ; and that apparent setbacks like stress can be enhancing rather than debilitating @xcite . instead of saying "i'm such a lazy procrastinator," a growth-mindset would say "i'm determined to learn better time management." this mindset has demonstrable benefits like improved performance on school tests @xcite @xcite . impermanence means understanding that negative experiences are finite and temporary, and that others have also experienced or even overcome similar forms of adversity. someone might say "since i failed this test, i must be too stupid for school." an impermanence reframe could be "this wasn't the test score i hoped for, but everyone slips up now and then." this category is also related to those proposed by walton and brady (2020): (1) focus on the "possibility of improvement," (2) recognize "specific, normal causes," and (3) under-stand "you're not the only one." neutralizing involves removing or rewriting negative phrases and terms so they are more neutral @xcite . someone might complain that "wendy's customer service is terrible." a neutralized reframe could be "wendy's customer service could use some improvement." optimism does not mean to negate or deny the negative aspects of a situation, but instead to shift the emphasis to the more positive aspects of the situation, including expectations for a bright future @xcite . for example, if there is a negative emphasis, like in the sentence, "i've completely worked myself to the bone this week, burning the candle at both ends... tgif," we can use optimism to shift the emphasis towards the positive as follows: "it's been a long week, but now i can kick back, relax, and enjoy my favorite shows because it's the weekend." self-affirmation means to assert a more holistic or expansive version of oneself by listing one's values, skills, and positive characteristics @xcite . positive psychology gives many examples like love, courage, hope, gratitude, patience, forgiveness, creativity, and humor @xcite . reflecting on these values can bolster one's sense of integrity (see self-affirmation theory; steele 1988), can reduce depressive affect @xcite , and can translate to increased performance on measurable tasks like exams @xcite @xcite . thankfulness can also be described more broadly as an "attitude of gratitude" @xcite . adding more positive words that convey thankfulness or gratitude (e.g. appreciate, glad that, thankful for). for example, we can reframe the rhetorical question ,"is it sad that i don't wanna be at home and wish that work could call me in early?" by expressing gratitude for career: "i am thankful that i have a job that makes me want to get out of bed everyday." ## data collection we sourced all of our data from the twitter api, filtering tweets according to the hashtag #stressed due to a few reasons. note that at the time of data collection and annotation, there were no publicly available datasets with annotated cognitive distortions, and the literature on distortion classification was still relatively unexplored @xcite . we instead chose the simple keyword #stressed to signal the anxiety, negative affect, and hopelessness that has been shown to accompany cognitive distortions by prior work @xcite . 1 our decision to use twitter was also motivated by the 280 character limit, which ensured that samples were short, focused expressions of relatively atomic ideas, as opposed to longer narrative-style texts from discussion platforms like reddit's r/rant. our filtered collection of negative texts comes from a collection of over 1 million #stressed tweets written between 2012 and 2021, and it excludes any replies and retweets, any insubstantial tweets less than 30 characters, and any text containing a url, which is often associated with spam @xcite . after we removed other hashtags or twitter handles from the text, we used textblob @xcite to exclude any overtly positive texts with a non-negative sentiment score. finally, to reduce any confounds between cognitive distortions and hate speech, and to make the human annotation task more agreeable for crowd-workers, we excluded examples that were flagged as offensive with over 80% confidence according to hatesonar @xcite . 1 we also considered pet peeve, fml, and other keywords but manual inspection revealed that these tweets were unlikely to contain cognitive distortions. in contrast, stressed hashtag provides a high precision data collection. we acknowledge this as a limitation and urge readers to keep this mind when interpreting our findings. ## positive reframing with positive psychology frames, we then examine how generative models work to automatically suggest a negatively-oriented self-narrative with a more positive shift in perspective without distorting any of the underlying meaning of that text. to do so will make use of encoder-decoder or conditional language models, as well as the six positive psychology strategies outlined in section 3. ## discussion and conclusion this work introduces a new and challenging nlg task called positive reframing. the objective is to construct a more positive outlook as a way of rephrasing a negative source text such that the meaning of that source is preserved. our parallel dataset, positive psychology frames, will serve as a benchmark that will enable sustained work on this task. we experiment with many of the leading style-transfer models and show that these models can learn to shift from a negative to a more positive perspective using a combination of strategies from positive psychology. importantly, the best models are fluent and effective reframing systems that can learn to largely preserve the meaning of the original text, even under a perspective shift. however, these models still struggle to generate reasonable positive perspectives, and even the best models are still prone to errors. we discuss four key error classes: insubstantial changes, contradictions to the premise, self-contradictions, and hallucinations, as shown in error analyses in section 5.5. overall, this suggests that our dataset can serve as a useful benchmark for understanding well-motivated positive reframing strategies and equipping natural language generation systems with positive perspectives. future work can dive deeper into these issues by enforcing a stronger level of semantic equivalence between the generation and the source text @xcite . even with semantic equivalence constraints, it would be necessary to also allow for the injection of new positive perspectives. methods ranging from guided sequence generation @xcite or semantic attention-guided decoding @xcite to pragmatic reconstruction @xcite and persona consistency @xcite may all be applicable in follow-up studies.