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, POSITIVEPSYCHOLOGYFRAMES , 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. To download the data, see https://github. com/GT-SALT/positive-frames 1 Introduction Gratitude is not only the greatest of virtues, but the parent of all the others. Marcus Tullius Cicero Text style transfer (TST) has received much attention from the language technologies community (Hovy, 1987; Jin et al., 2020), where the goal is to change some attribute, like the sentiment of the text, without changing any attribute-independent content (Mir et al., 2019; Fu et al., 2018; Logeswaran et al., 2018). Some TST applications such as de-biasing (Pryzant et al., 2020; Ma et al., 2020) and paraphrasing (den Bercken et al., 2019; Xu et al., 2012) require meaning-preserving transformations, while political leaning (Prabhumoye et al., 2018), sentiment (Shen et al., 2017; Hu et al., 2017), and topical transfer (Huang et al., 2020) allow for a change (cid:63) Equal contribution. in the underlying meaning. For instance, for a negative review,  this was a bland dish , we can use a sentiment TST model to create a more positive  this was a tasty dish , by swapping the word bland with tasty. Although the input's structure and attribute-independent content are preserved, the truth-conditional meaning is clearly altered. In this work, we introduce a closely related task positive reframingthat 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 (Harris et al. 2007; see Section 3). To use the example from before, we could reframe  this was a bland dish  with the self-affir-mation 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 (Grice, 1975), but it shifts the focus away from the negative judgment and onto a positive and 3682 self-affirming perspective. Numerous studies have shown the positive effects of this and other reframing strategies on well-being and cognitive performance (Martens et al., 2006; Cohen et al., 2006; Good et al., 2003), 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 theoretically-motivated re-write strategies. This is a significant contribution, especially since rich parallel corpora are scarce in TST tasks. Some related datasets exist for politeness (Madaan et al., 2020) and sentiment transfer (Shen et al., 2017; He and McAuley, 2016), 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 (Lample et al., 2019). Using our parallel corpus, we examine how current state-of-the-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. There is a longstanding interest in style transfer, starting with the early days schema-based systems (McDonald and Pustejovsky, 1985; Hovy, 1987), and then syntax-based (Zhu et al., 2010; Xu et al., 2016) and phrase-based machine translation (Xu et al., 2012; Wubben et al., 2012), into the age of end-to-end neural models. Recent works include supervised seq2seq tasks on parallel data (Rao and Tetreault, 2018; Fu et al., 2018) or pseudo-parallel data (Jin et al., 2019; Zhang et al., 2020b), as well as unsupervised generative modeling on nonparallel data (Hu et al., 2017; Shen et al., 2017), and semi-supervised techniques (Shang et al., 2019). Other ideas include domain adaptation (Li et al., 2019) or multi-task learning (Niu et al., 2018), zero-shot translation (Korotkova et al., 2019), unsupervised delete and generate approaches (Li et al., 2018; Sudhakar et al., 2019; Malmi et al., 2020; Madaan et al., 2020), and reinforcement learning (Zhang and Lapata, 2017; Wang et al., 2016). Many existing datasets lack parallel structure, so the unsupervised setting is common in TST. Unfortunately, many of these methods still fail to disentangle style from content and adequately preserve the meaning of the original text (Lample et al., 2019). Autoencoders are particularly vulnerable to this shortcoming (Hu et al., 2017; Zhao et al., 2018), but some unsupervised machine translation techniques appear less vulnerable (Artetxe et al., 2018; Lample et al., 2018). In contrast, our positive reframing task requires source meaning-preservation and the introduction of new content and new perspectives, posing a unique challenge to unsupervised methods. We also provide a parallel corpus to train supervised models for this task. Positivity is contagious and can spread quickly across social networks (Coviello et al., 2014; Hat-field et al., 1993). Positive contagion in teams can reduce group conflict and improve group cooperation and even task performance (Barsade, 2002). Effective leaders also harness the power of positive reframing to promote company growth (Sy and Choi, 2013; Sy et al., 2005; Johnson, 2009; Masters, 1992) and beneficially shape negotiations (Filipowicz et al., 2011), customer relations (Dietz et al., 2004), decision making (Gchter et al., 2009; Druckman, 2001) and policy outcomes (Erisen et al., 2014). At an individual level, people who express optimism and gratitude are less likely to have depressive symptoms (Lambert et al., 2012) and more likely to experience emotional and psychological well-being (Carver et al., 1999; Watkins et al., 2008; Scheier et al., 2001). On the other hand, fake expressions of positivity are correlated with negative brain activity (Ekman et al., 1990) and may actually be more harmful than helpful (Fredrickson, 2000; Fredrickson and Losada, 2005; Gross, 2013; Logel et al., 2009). That is why in our task it is essential that any positively reframed rephrased text remain true to the original premise of the source. In this way, our task is most similar to meaning-preserving transformations via parallel corpora from domains such as political argumentation (Chakrabarty et al., 2021), de-biasing (Pryzant et al., 2020; Ma et al., 2020), politeness (Madaan et al., 2020), and paraphrasing 3683 (den Bercken et al., 2019; Xu et al., 2012). In this section, we present our psychologically-motivated 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 self-narratives, which are known in the literature as cognitive distortions (Burns, 1981; Abramson et al., 2002; Walton and Brady, 2020). Cognitive distortions include many exaggerated or irrational self-focused thoughts (Nalabandian and Ireland, 2019), such as dichotomous all-or-nothing thinking (Oshio, 2012), over-generalization (Muran and Motta, 1993), and catastrophizing (Sullivan et al., 2001). We can reconstruct these ideas using strategies from positive psychology (Harris et al., 2007). 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 (Yeager et al., 2014; Burnette and Finkel, 2012), is the belief that one's skills and abilities are not immutable but can instead be changed and improved over time (Dweck, 2016); that one's willpower is an abundant rather than limited or exhaustible resource (Job et al., 2010, 2015); and that apparent setbacks like stress can be enhancing rather than debilitating (Crum et al., 2013). 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 (Good et al., 2003; Blackwell et al., 2007; Dweck and Yeager, 2019; Yeager et al., 2014). 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) understand you're not the only one. Neutralizing involves removing or rewriting negative phrases and terms so they are more neutral (Pryzant et al., 2020). 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 (Carver et al., 2010). 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 (Cohen and Sherman, 2014; Silverman et al., 2013). Positive psychology gives many examples like love, courage, hope, gratitude, patience, forgiveness, creativity, and humor (Harris et al., 2007). Reflecting on these values can bolster one's sense of integrity (see Self-Affirmation Theory; Steele 1988), can reduce depressive affect (Enright and Fitzgibbons, 2000), and can translate to increased performance on measurable tasks like exams (Martens et al., 2006; Cohen et al., 2006; Sherman et al., 2009). Thankfulness can also be described more broadly as an attitude of gratitude (Emmons and Shelton, 2002). 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 . 4 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 3684 Label Distribution Count Label Description ICC Gen 25.4% 2,120 Growth Mindset Viewing a challenging event as an opportunity for the author specifically to grow or improve themselves. cognitive distortions, and the literature on distortion classification was still relatively unexplored (Simms et al., 2017; Shickel et al., 2020). 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 (Sears and Kraus, 2009). 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 (Zhang et al., 2012; Grier et al., 2010). After we removed other hashtags or Twitter handles from the text, we used TextBlob (Loria, 2018) 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 (Davidson et al., 2017). 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. We recruited crowdworkers to reframe 8,687 randomly-sampled texts with two workers assigned to each task, so we had two unique reframe annotations for every tweet. The annotators were encouraged to decide independently which reframing strategy to use, and they could combine multiple strategies in the same reframe. We simply asked annotators to record the strategies they selected. Additionally, they gave us, on a scale from 1-5, a score indicating how positive the original text was, and separately, how positive the text had become after they reframed it. Finally, we asked workers to mark advertisements, spam, or any text they felt they could not understand or effectively reframe. These examples were later removed from the corpus (see Appendix A for details). In total, 204 workers participated in this task. Before they worked on the task, workers were asked to be familiar with our task by reading our provided reframing examples for each of the six strategies (Section 3), along with detailed annotation instructions. Then they had to pass a qualification test to show they can recognize different strategies in different reframing examples, with at least 5 out of 6 multiple-choice questions answered correctly. We paid all annotators a fair wage above the federal minimum and both manually and programmatically inspected their work for quality (see Appendix A). After removing any poor-quality data, we were left with 8,349 reframed sentences. The strategy label distribution is given on the left side of Table 1, where a single reframe can have more than one strategy label. To determine the reliability of the reframing strategy constructs, we randomly sampled 100 annotations from Section 4.1 and asked three annotators to consider both the original text and the reframed text, and then the annotators marked which of the six strategies were used in the given reframe. This allowed us to compute inter-annotator agreement scores for the strategy labels in Table 1. We observe the Intra-class Correlation for one-way random effects between the three raters and find moderate inter-rater agreement across these attribute categories (min 0.32; max 68). We also asked this second round of annotators to evaluate the genuineness of the reframes on a scale from 1-5. Our instructions explain that, with a more genuine reframe, it is more likely that someone in the original situation would say something similar. We find that, across all strategy labels, the average genuineness score is  4 out of 5, so we know the data conforms reasonably well to our task instructions. With POSITIVEPSYCHOLOGYFRAMES , 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. Let ( s, t,  t ) be a single annotation tuple in POSITIVEPSYCHOLOGYFRAMES for original source text s and positive reframe target t , which uses positive psychology strategies given by the multi-hot encoded vector  t . In the Positive Reframing task, our goal is to encode s and, at decoding time, produce t which makes use of  t strategies and preserves the underlying meaning of s . Therefore, we formulate the problem as conditional generation and, during training, we maximize the standard language modeling objective 1 NN (cid:88) i =0 log p ( g i | g 0: i  1 ) over the string g = { s,  t , t } = { <BOS> , s 1 , s 2 , ..., s n , <STRG> ,  grow ,  imp , ...,  thank , <REFR> , t 1 , t 2 , ..., t m , <EOS> } where g i is the i th token in the string of length N , which contains the start token <BOS> , the tokenized source s 1: n , the tokenized reframe target t 1: m , and the binary tokens  grow ,  imp , ... indicating whether a particular strategy (e.g. grow th mindset) was used in reframe t . At decoding time, we consider three settings: Unconstrained generation p ( t | s ) , Controlled generation p ( t | s,  t ) , and a strategy Prediction form of generation p ( t,  t | s ) . Unlike in the Unconstrained setting, the Controlled generation is conditioned on the desired strategies  t . In the Prediction setting, the model will concurrently predict the strategies it used to generate its own reframe. Note that, we introduce three different model settings here to capture how positive reframing assistance might be used by people in the real world. Specifically, the Unconstrained setting models reframing text directly without being aware of any specific strategy to use. The Prediction setting extends the unconstrained mode, i.e., produce the reframed text and also output the reframing strategies used in the reframing process spontaneously. The Controlled setting simulates the scenario of producing a reframed text with the help of concrete positive reframing strategies. For ground truth training, development, and testing, we randomly partition the annotations using an 8:1:1 ratio, with 6,679 train, 835 development and 835 test data. We fine-tune the GPT and GPT-2 language models (Radford et al., 2019) as well as two Seq2Seq neural machine translation models  LSTM (Hochreiter and Schmidhuber, 1997) and CopyNMT (See et al., 2017)  and finally, two encoder-decoder models, BART (Lewis et al., 2020) and T5 (Raffel et al., 2020). For all models, we use greedy decoding. As an ablation in the Unconstrained setting, we also test a No-pretrain condition for GPT-2 in which we randomly initialize the model parameters before fine-tuning. Retrieval: We test two simple retrieval systems: Random retrieval of a reframed sentence from the training set, and SBERT (Reimers and Gurevych, 3686 Automatic Evaluation Human Evaluation Model R-1 R-2 R-L BLEU BScore  TB Avg. 2019) retrieval, which finds the most similar t in train by cosine similarity and retrieves one of the corresponding ground-truth r from the training set. Few-shot Learning: Brown et al. (2020) shows the few-shot capabilities of language models and especially larger models like GPT-3. We evaluate few-shot abilities of both GPT-3 and its open-source implementation, GPT-Neo (Black et al., 2021) using k = 5 exemplars (See Appendix C). Following other style transfer work with a parallel corpus (Jhamtani et al., 2017; Xu et al., 2012), we evaluate our models for semantic similarity with the ground truth using the BLEU (Pa-pineni et al., 2002), ROUGE (Lin, 2004), and BERTScore (Zhang et al., 2020a). Since there are two ground truth annotations per tweet, we take the maximum of the two scores and report the average across these maxima. We also report  T extBlob or the average change in sentiment score according to TextBlob (Loria, 2018). Finally, we conduct human evaluation in which 50 items are distributed to 3 raters who score the reframed sentences for three criteria, each on a scale from 1 to 5. The criteria include Meaning Preservation (Shang et al., 2019), our task-specific objective, as well as the Positivity and Fluency of the generated text, following the sentiment style transfer literature (Luo et al., 2019) 5.4 Results Automatic Evaluation Across these metrics (Ta-ble 2, left ) in the unconstrained generation setting, the BART model provided the highest quality of positive reframes, while GPT provided the worst quality with results similar to the No-pretrain version of GPT-2. The pre-trained version of GPT-2 was trained on English web text, while GPT was trained on works of fiction, so it appears that pretraining decisions can affect performance. We tested the two best-performing models, T5 and BART, on the controlled generation and strategy-prediction settings as well and found that the both models performed reasonably. Overall, controlled generation boosts performance, since the model can target the gold standard's strategies, but these improvements are only slight (see the Controlled part in Table 2). This warrants further 3687 investigation: in Section 5.6, we explore models' ability to identify the underlying strategies given an existing reframe to understand whether models can make sense of these underlying constructs. Unsurprisingly, all supervised models outperformed our simple retrieval baselines. Most interestingly, few-shot GPT-3 and GPT-Neo also could not match the supervised models in terms of overlap with the ground truth (ROUGE, BLEU, BERTScore), but they still achieved a comparable positive shift in sentiment (  TextBlob). Human Evaluation Human judgments both support and elaborate on the automatic evaluation find-ings. For our best performing BART and T-5 models, the average scores are very high, even surpassing the quality of the Human gold standard in all of the unconstrained, predictive, and controlled settings. These systems most effectively induce a natural-sounding positive reframe while also preserving the meaning of the original text. This is critical: controlled BART model scored 4.07 in Positivity and 4.27 in Fluency while also achieving the winning Meaning preservation score. In contrast with BART, the few-shot systems fail to preserve the meaning of the original sentence, despite their ability to articulately induce a more positive sentiment (Positivity scores up to 4.17; Fluency scores up to 4.27). Meaning preservation is absolutely critical for this task. From these results, we can conclude that, at the present time, supervised learning may be the most viable option for achieving reliable positive reframing results. POSITIVEPSYCHOLOGYFRAMES will facilitate ongoing efforts in this direction. Qualitative Investigation Table 3 shows example reframes generated by our best controlled BART model, with one example for each strategy (for a similar comparison between models , see Table 5 in Appendix D). We see that, even without explicit lexical overlap between the generation and ground truth, the model reframes can still shift the cognitive distortions and negative outlook to a more positive perspective. In each of these examples, the model does so without losing the underlying meaning of the original text. Transformer-based models appear to be capable of solving our task with reasonable success. However, success can be highly variable (as evidenced by Table 5), so there is still room for significant improvement. We manually go through 100 randomly sampled model generations by our best controlled BART model, and summarize the main error classes here. We manually investigated 100 randomly sampled model generations by our best controlled BART model, and summarize the four largest error classes here. First, 26% of generations contained (1) insubstantial changes . These were especially prominent in the neutralizing strategy where the model would swap only a few negative words, like changing the phrase  I hate it  to  I don't like it.  On the other hand, some reframed generations were so drastically modified they contained (2) contradictions to the premise (9% of instances). For example, " Feel like crying, this math class is impossible to pass " was transformed into " This math class is hard, but I know I can pass it "  a failure of meaning preservation. More concerningly, the system can generate (3) self-contradictions (6%) like the phrase, " I don't like opening up to people, but I'm glad I have the courage to do it. " Finally, like many other NLG systems, our system can produce (4) hallucinations (2%) with unmotivated perspectives, like mentioning a good night sleep when the original post was about nosebleeds in the bath. In Section 5.4, we observed only slight performance gains when conditioning the generation based on the ground-truth reframing strategy ( Control section in Table 2). For this reason, we take a closer look at whether models can reliably understand and classify the reframe strategies underlying a given source-reframe text pair. We formulate this problem as a multi-label multi-class classification task over sentence pairs ( s, t ) . Given both the source text and positive reframe target in the annotation tuple ( s, t ) from POSITIVEPSYCHOLOGYFRAMES , we predict the multi-hot encoded strategy vector  t = [ s grow ; s imp ; ... ; s thank ] using transformer models. We experiment with a set of state-of-the-art classifiers, including BERT (De-vlin et al., 2019), RoBERTA (Liu et al., 2019), and XLNet (Yang et al., 2019). As shown in Table 4, all of the classification models can learn to recognize the thankfulness, optimism, and growth mindset strategies with moderate reliability ( F 1 > 0 . 60) . Although XLNet model cannot identify the neutralizing strategy very well, BERT and RoBERTa models can achieve an 3688 Original Text Strategy Positive Reframe F1 score of around 0.6. The impermanence and self-affirmation strategies appear more challenging for all three models to identify. Overall, the results here show that this task is tractable: reframe strategies are learnable by various classification models. This further supports the reliability of our Positive Psychology framework, confirming what we found with human reliability metrics in Section 4.2. Although we mainly treat this frame strategy classification as a robustness check and deep dive into the role of framing strategies, this task can also be a novel NLP or computational social science application on its own, i.e., determining the positive reframing relation between a pair of sentences. 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, POSITIVEPSYCHOLOGYFRAMES , 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 3689 (Nie et al., 2019). 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 (Krause et al., 2020) or semantic attention-guided decoding (Nie et al., 2019) to pragmatic reconstruction (Shen et al., 2019) and persona consistency (Kim et al., 2020) may all be applicable in follow-up studies. The authors would like to thank reviewers for their helpful insights and feedback. CZ is supported by the NSF Graduate Research Fellowship under Grant No. DGE-2039655 and DY is supported by the Microsoft Research Faculty Fellowship. This work is funded in part by a grant from Amazon. Annotation. We followed the guidelines for ethical annotation practices and crowdsourcing that are outlined in (Sheehan, 2018), including paying workers a fair wage above the federal minimum. If workers contacted us with any questions or concerns, we responded promptly to them within 24 hours. In the task interface, in the header, we warned annotators that the content might be upsetting, and we gave the following recommendation:  if any point you do not feel comfortable, please feel free to skip the HIT or take a break. . Deployment. Although this data is designed for pro-social outcomes (i.e. increasing positivity in text), there may be unexpected use-cases for this data, such as obfuscating impolite or even hateful data to avoid detection (ElSherief et al., 2021). The parallel structure of the data means it is also possible to invert the direction of the seq2seq task to introduce more negative or pessimistic perspectives into a positive source. This is not a particularly new risk, since sentiment style transfer can accomplish a similar outcome in this direction. Still, we will require interested parties to sign a data-use agreement that encourages only ethical uses of POSITIVEPSYCHOLOGYFRAMES .
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