jagomart
digital resources
picture1_Positive Reframing Examples Pdf 88265 | Acl Lon257


 200x       Filetype PDF       File size 1.03 MB       Source: aclanthology.org


File: Positive Reframing Examples Pdf 88265 | Acl Lon257
inducing positive perspectives with text reframing caleb ziems minzhili anthonyzhang diyi yang georgia institute of technology cziems azhang305 dyang888 gatech edu national university of singapore li minzhi u nus edu ...

icon picture PDF Filetype PDF | Posted on 15 Sep 2022 | 3 years ago
Partial capture of text on file.
                                      Inducing Positive Perspectives with Text Reframing
                                Caleb Ziems ⋆†            MinzhiLi⋆⋄             AnthonyZhang†                Diyi Yang †
                                                            †Georgia Institute of Technology
                                           {cziems, azhang305, dyang888}@gatech.edu
                                                           ⋄National University of Singapore
                                                               li.minzhi@u.nus.edu
                                           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 neu-
                        tralize a negative point of view and generate a
                        more positive perspective for the author with-
                        out contradicting the original meaning. Our in-
                        sistence on meaning preservation makes posi-
                        tive reframing a challenging and semantically
                        rich task. To facilitate rapid progress, we intro-         Figure 1: Positive reframing vs. negative-to-positive
                        duce a large-scale benchmark, POSITIVE PSY-                sentiment style transfer.
                        CHOLOGYFRAMES,with8,349sentencepairs
                        and 12,755 structured annotations to explain
                        positive reframing in terms of six theoretically-          in the underlying meaning. For instance, for a
                        motivated reframing strategies. Then we eval-              negative review, “this was a bland dish,” we can
                        uate a set of state-of-the-art text style trans-           use a sentiment TST model to create a more posi-
                        fer models, and conclude by discussing key                 tive “this was a tasty dish,” by swapping the word
                        challenges and directions for future work. To              bland with tasty. Although the input’s structure
                        download the data, see https://github.
                        com/GT-SALT/positive-frames                                and attribute-independent content are preserved,
                                                                                   the truth-conditional meaning is clearly altered.
                    1 Introduction                                                    Inthiswork,weintroduceacloselyrelatedtask—
                          Gratitude is not only the greatest of                    positive reframing—that differs from sentiment
                          virtues, but the parent of all the others.               TST in important ways. We effectively reframe
                                   —MarcusTulliusCicero                            negative text by inducing a complementary pos-
                                                                                   itive viewpoint (e.g. glass-half-full), which nev-
                       Text style transfer (TST) has received much at-             ertheless supports the underlying content of the
                    tention from the language technologies community               original sentence. The reframe should implicate
                    (Hovy, 1987; Jin et al., 2020), where the goal is to           rather than contradict the source (see Figure 1),
                    changesomeattribute,likethesentimentofthetext,                 and the transformation should be motivated by the-
                    withoutchanginganyattribute-independentcontent                 oretically justified strategies from from positive
                    (Mir et al., 2019; Fu et al., 2018; Logeswaran et al.,         psychology (Harris et al. 2007; see Section 3).
                    2018). Some TST applications such as de-biasing                   To use the example from before, we could re-
                    (Pryzant et al., 2020; Ma et al., 2020) and para-              frame “this was a bland dish” with the self-affir-
                    phrasing (den Bercken et al., 2019; Xu et al., 2012)           mation strategy and say “I’ve made dishes that are
                    require meaning-preserving transformations, while              muchtastier than this one.” This reframed one still
                    political leaning (Prabhumoye et al., 2018), senti-            communicates the author’s original intention by
                    ment (Shen et al., 2017; Hu et al., 2017), and topi-           conversationally implicating that the dish was un-
                    cal transfer (Huang et al., 2020) allow for a change           satisfying (Grice, 1975), but it shifts the focus away
                        ⋆Equal contribution.                                       fromthenegative judgment and onto a positive and
                                                                              3682
                                      Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics
                                                            Volume 1: Long Papers, pages 3682 - 3700
                                                                  c
                                                May22-27,2022
2022AssociationforComputationalLinguistics
                  self-affirming perspective. Numerous studies have          2018; Sudhakar et al., 2019; Malmi et al., 2020;
                  shownthe positive effects of this and other refram-       Madaanetal., 2020), and reinforcement learning
                  ing strategies on well-being and cognitive perfor-        (Zhang and Lapata, 2017; Wang et al., 2016).
                  mance (Martens et al., 2006; Cohen et al., 2006;            Manyexisting datasets lack parallel structure, so
                  Goodetal., 2003), which motivate this work.               the unsupervised setting is common in TST. Un-
                     Our main contribution is the design and imple-         fortunately, many of these methods still fail to dis-
                  mentation of a new positive reframing task. To            entangle style from content and adequately pre-
                  facilitate research in this space, we introduce a par-    serve the meaning of the original text (Lample
                  allel corpus of 8,349 reframed sentence pairs and         et al., 2019). Autoencoders are particularly vul-
                  12,755 structured annotations for six theoretically-      nerable to this shortcoming (Hu et al., 2017; Zhao
                  motivated re-write strategies. This is a significant       et al., 2018), but some unsupervised machine trans-
                  contribution, especially since rich parallel corpora      lation techniques appear less vulnerable (Artetxe
                  are scarce in TST tasks. Some related datasets exist      et al., 2018; Lample et al., 2018). In contrast, our
                  for politeness (Madaan et al., 2020) and sentiment        positive reframing task requires source meaning-
                  transfer (Shen et al., 2017; He and McAuley, 2016),       preservation and the introduction of new content
                  but they lack this parallel structure. With only un-      and new perspectives, posing a unique challenge to
                  aligned corpora, researchers are limited to unsuper-      unsupervised methods. We also provide a parallel
                  vised training paradigms, which notoriously fail to       corpus to train supervised models for this task.
                  disentangle style from content, and thus also fail to
                  preserve meaning (Lample et al., 2019). Using our         2.2   LanguageandPositivePsychology
                  parallel corpus, we examine how current state-of-         Positivity is contagious and can spread quickly
                  the-art neural models work for positive reframing.        across social networks (Coviello et al., 2014; Hat-
                  Wefindthat,supervised transformer-based neural             field et al., 1993). Positive contagion in teams can
                  models appear capable of rewriting a negative text        reduce group conflict and improve group cooper-
                  without contradicting the original premise of that        ation and even task performance (Barsade, 2002).
                  text. However, these models still struggle to gen-        Effective leaders also harness the power of pos-
                  erate reasonable positive perspectives, suggesting        itive reframing to promote company growth (Sy
                  that our dataset will serve as a useful benchmark         and Choi, 2013; Sy et al., 2005; Johnson, 2009;
                  for understanding psychologically well-motivated          Masters, 1992) and beneficially shape negotiations
                  strategies for augmenting text with positive per-         (Filipowicz et al., 2011), customer relations (Dietz
                  spectives.                                                et al., 2004), decision making (Gächter et al., 2009;
                  2 RelatedWork                                             Druckman, 2001) and policy outcomes (Erisen
                                                                            et al., 2014). At an individual level, people who
                  2.1   Style-Transfer                                      express optimism and gratitude are less likely to
                  There is a longstanding interest in style transfer,       have depressive symptoms (Lambert et al., 2012)
                  starting with the early days schema-based systems         and more likely to experience emotional and psy-
                  (McDonald and Pustejovsky, 1985; Hovy, 1987),             chological well-being (Carver et al., 1999; Watkins
                  and then syntax-based (Zhu et al., 2010; Xu et al.,       et al., 2008; Scheier et al., 2001).
                  2016) and phrase-based machine translation (Xu              Ontheother hand, fake expressions of positivity
                  et al., 2012; Wubben et al., 2012), into the age of       are correlated with negative brain activity (Ekman
                  end-to-end neural models. Recent works include            et al., 1990) and may actually be more harmful
                  supervised seq2seq tasks on parallel data (Rao and        than helpful (Fredrickson, 2000; Fredrickson and
                  Tetreault, 2018; Fu et al., 2018) or pseudo-parallel      Losada, 2005; Gross, 2013; Logel et al., 2009).
                  data (Jin et al., 2019; Zhang et al., 2020b), as          That is why in our task it is essential that any pos-
                  well as unsupervised generative modeling on non-          itively reframed rephrased text remain true to the
                  parallel data (Hu et al., 2017; Shen et al., 2017), and   original premise of the source. In this way, our
                  semi-supervised techniques (Shang et al., 2019).          task is most similar to meaning-preserving transfor-
                  Other ideas include domain adaptation (Li et al.,         mations via parallel corpora from domains such as
                  2019)ormulti-tasklearning(Niuetal.,2018),zero-            political argumentation (Chakrabarty et al., 2021),
                  shot translation (Korotkova et al., 2019), unsuper-       de-biasing (Pryzant et al., 2020; Ma et al., 2020),
                  vised “delete and generate” approaches (Li et al.,        politeness (Madaan et al., 2020), and paraphrasing
                                                                       3683
                 (den Bercken et al., 2019; Xu et al., 2012).            stand “you’re not the only one.”
                 3 Positive Reframing Framework                          Neutralizing    involves removing or rewriting
                                                                         negative phrases and terms so they are more neu-
                 In this section, we present our psychologically-        tral (Pryzant et al., 2020). Someone might com-
                 motivated taxonomy of positive reframing strate-        plain that “Wendy’s customer service is terrible.” A
                 gies.    Instead of merely swapping antonyms            neutralized reframe could be “Wendy’s customer
                 for negative words or inserting unfounded pos-          service could use some improvement.”
                 itive language into a sentence, these strategies        Optimism doesnotmeantonegateordenythe
                 work to more fundamentally reconstruct the au-          negative aspects of a situation, but instead to shift
                 thor’s fixed, global, and ultimately harmful self-       the emphasis to the more positive aspects of the
                 narratives, which are known in the literature as cog-   situation, including expectations for a bright fu-
                 nitive distortions (Burns, 1981; Abramson et al.,       ture (Carver et al., 2010). For example, if there
                 2002; Walton and Brady, 2020). Cognitive dis-           is a negative emphasis, like in the sentence, “I’ve
                 tortions include many exaggerated or irrational         completely worked myself to the bone this week,
                 self-focused thoughts (Nalabandian and Ireland,         burning the candle at both ends... TGIF,” we can
                 2019), such as dichotomous “all-or-nothing” think-      use optimism to shift the emphasis towards the pos-
                 ing (Oshio, 2012), over-generalization (Muran and       itive as follows: “It’s been a long week, but now I
                 Motta, 1993), and catastrophizing (Sullivan et al.,     can kick back, relax, and enjoy my favorite shows
                 2001). We can reconstruct these ideas using strate-     because it’s the weekend.”
                 gies from positive psychology (Harris et al., 2007).
                 Each strategy is designed to promote a beneficial        Self-affirmation     meanstoassert a more holistic
                 shift in perspective without distorting the underly-    or expansive version of oneself by listing one’s
                 ing context of the author’s situation.                  values, skills, and positive characteristics (Cohen
                 GrowthMindset or,alternatively, the incremen-           and Sherman, 2014; Silverman et al., 2013). Pos-
                 tal theory of personality (Yeager et al., 2014; Bur-    itive psychology gives many examples like love,
                 netteandFinkel,2012),isthebeliefthatone’sskills         courage, hope, gratitude, patience, forgiveness, cre-
                 and abilities are not immutable but can instead be      ativity, and humor (Harris et al., 2007). Reflecting
                 changed and improved over time (Dweck, 2016);           on these values can bolster one’s sense of integrity
                 that one’s willpower is an abundant rather than         (see Self-Affirmation Theory; Steele 1988), can
                 limited or exhaustible resource (Job et al., 2010,      reduce depressive affect (Enright and Fitzgibbons,
                 2015); and that apparent setbacks like stress can       2000), and can translate to increased performance
                 be enhancing rather than debilitating (Crum et al.,     on measurable tasks like exams (Martens et al.,
                 2013). Instead of saying “I’m such a lazy pro-          2006; Cohen et al., 2006; Sherman et al., 2009).
                 crastinator,” a growth-mindset would say “I’m de-       Thankfulness      can also be described more
                 termined to learn better time management.” This         broadly as an “attitude of gratitude” (Emmons and
                 mindset has demonstrable benefits like improved          Shelton, 2002). Adding more positive words that
                 performance on school tests (Good et al., 2003;         convey thankfulness or gratitude (e.g. appreciate,
                 Blackwell et al., 2007; Dweck and Yeager, 2019;         glad that, thankful for). For example, we can re-
                 Yeager et al., 2014).                                   frame the rhetorical question ,“Is it sad that I don’t
                 Impermanence meansunderstandingthatnega-                wanna be at home and wish that work could call
                 tive experiences are finite and temporary, and that      meinearly?” byexpressing gratitude for career: “I
                 others have also experienced or even overcome           amthankful that I have a job that makes me want
                 similar forms of adversity. Someone might say           to get out of bed everyday.”
                 “since I failed this test, I must be too stupid for     4 DataCollection
                 school.” An impermanence reframe could be “This
                 wasn’t the test score I hoped for, but everyone slips   We sourced all of our data from the Twitter
                 up now and then.” This category is also related         API, filtering tweets according to the hashtag
                 to those proposed by Walton and Brady (2020):           #stressed due to a few reasons. Note that at
                 (1) focus on the “possibility of improvement,” (2)      the time of data collection and annotation, there
                 recognize “specific, normal causes,” and (3) under-      were no publicly available datasets with annotated
                                                                     3684
                                Label Distribution     Count    Label               Description                                                      ICC Gen
                        25.4%                          2,120    GrowthMindset       Viewing a challenging event as an opportunity for the author     0.59   3.77
                                                                                    specifically to grow or improve themselves.
                        19.5%                          1,625    Impermanence        Saying bad things don’t last forever, will get better soon,      0.60   4.03
                                                                                    and/or that others have experienced similar struggles.
                        36.1%                          3,015    Neutralizing        Replacing a negative word with a neutral word. For example,      0.32   3.53
                                                                                    “This was a terrible day” becomes “This was a long day.”
                        48.7%                          4,069    Optimism            Focusing on things about the situation itself, in that moment,   0.44   3.89
                                                                                    that are good (not just forecasting a better future).
                        10.1%                          841      Self-affirmation     Talking about what strengths the author already has, or the      0.42   3.75
                                                                                    values they admire, like love, courage, perseverance, etc.
                        13.0%                          1,085    Thankfulness        Expressing thankfulness or gratitude with key words like         0.68   3.95
                                                                                    appreciate, glad that, thankful for, good thing, etc.
                      Table 1: Summary statistics for POSITIVE PSYCHOLOGY FRAMES. (Left) Distribution of the non-exclusive labels across all
                      8,349 annotations shows a preference for optimism and neutralizing strategies. (Right) The quality of annotations is shown by
                      moderate Intra-class Correlation (ICC), with reasonable genuineness (Gen) metrics for 100 randomly sampled datapoints.
                      cognitive distortions, and the literature on distor-                     4.1     Annotation
                      tion classification was still relatively unexplored
                      (Simms et al., 2017; Shickel et al., 2020). We in-                       We recruited crowdworkers to reframe 8,687
                      stead chose the simple keyword #stressed to                              randomly-sampledtextswithtwoworkersassigned
                      signal the anxiety, negative affect, and hopeless-                       to each task, so we had two unique reframe anno-
                      ness that has been shown to accompany cognitive                          tations for every tweet. The annotators were en-
                      distortions by prior work (Sears and Kraus, 2009).1                      couraged to decide independently which reframing
                      Ourdecision to use Twitter was also motivated by                         strategy to use, and they could combine multiple
                      the 280 character limit, which ensured that samples                      strategies in the same reframe. We simply asked
                      wereshort,focusedexpressionsofrelativelyatomic                           annotators to record the strategies they selected.
                      ideas, as opposed to longer narrative-style texts                        Additionally, they gave us, on a scale from 1-5, a
                      from discussion platforms like Reddit’s r/rant.                          score indicating how positive the original text was,
                          Our filtered collection of negative texts comes                       and separately, how positive the text had become
                      from a collection of over 1 million #stressed                            after they reframed it. Finally, we asked workers
                      tweets written between 2012 and 2021, and it ex-                         to mark advertisements, spam, or any text they felt
                      cludes any replies and retweets, any insubstantial                       they could not understand or effectively reframe.
                      tweets less than 30 characters, and any text contain-                    These examples were later removed from the cor-
                      ing a URL, which is often associated with spam                           pus (see Appendix A for details).
                      (Zhang et al., 2012; Grier et al., 2010). After we                           Intotal, 204 workers participated in this task. Be-
                      removed other hashtags or Twitter handles from                           fore they worked on the task, workers were asked
                      the text, we used TextBlob (Loria, 2018) to exclude                      to be familiar with our task by reading our provided
                      any overtly positive texts with a non-negative sen-                      reframing examples for each of the six strategies
                      timent score. Finally, to reduce any confounds be-                       (Section 3), along with detailed annotation instruc-
                      tween cognitive distortions and hate speech, and to                      tions. Then they had to pass a qualification test
                      makethehumanannotationtaskmoreagreeablefor                               to show they can recognize different strategies in
                      crowd-workers, we excluded examples that were                            different reframing examples, with at least 5 out of
                      flagged as offensive with over 80% confidence ac-                          6 multiple-choice questions answered correctly.
                      cording to HateSonar (Davidson et al., 2017).                                We paid all annotators a fair wage above the
                                                                                               federal minimum and both manually and program-
                                                                                               matically inspected their work for quality (see Ap-
                          1Wealso considered pet peeve, fml, and other keywords                pendix A). After removing any poor-quality data,
                      but manual inspection revealed that these tweets were unlikely           wewereleft with 8,349 reframed sentences. The
                      to contain cognitive distortions. In contrast, stressed hashtag          strategy label distribution is given on the left side
                      provides a high precision data collection. We acknowledge                of Table 1, where a single reframe can have more
                      this as a limitation and urge readers to keep this mind when
                      interpreting our findings.                                                than one strategy label.
                                                                                          3685
The words contained in this file might help you see if this file matches what you are looking for:

...Inducing positive perspectives with text reframing caleb ziems minzhili anthonyzhang diyi yang georgia institute of technology cziems azhang dyang gatech edu national university singapore li minzhi u nus abstract sentiment transfer is one popular example a style task where the goal to reverse polarity reversal comes also in meaning we introduce different but related called which neu tralize negative point view and generate more perspective for author out contradicting original our sistence on preservation makes posi tive challenging semantically rich facilitate rapid progress intro figure vs duce large scale benchmark psy chologyframes sentencepairs structured annotations explain terms six theoretically underlying instance motivated strategies then eval review this was bland dish can uate set state art trans use tst model create fer models conclude by discussing key tasty swapping word challenges directions future work although input s structure download data see https github com gt sa...

no reviews yet
Please Login to review.