Dealing with Controversy: An Emotion and Coping Strategy Corpus Based on Role Playing

• URL: http://arxiv.org/abs/2409.19025v1
• Date: Thu, 26 Sep 2024 06:49:54 GMT
• Title: Dealing with Controversy: An Emotion and Coping Strategy Corpus Based on Role Playing
• Authors: Enrica Troiano, Sofie Labat, Marco Antonio Stranisci, Viviana Patti, Rossana Damiano, Roman Klinger,
• Abstract summary: Many emotion fundamentals remain under-explored in natural language processing. We treat emotions as strategies to cope with salient situations. We introduce the task of coping identification, together with a corpus to do so, constructed via role-playing.
• Score: 14.255172744243541
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There is a mismatch between psychological and computational studies on emotions. Psychological research aims at explaining and documenting internal mechanisms of these phenomena, while computational work often simplifies them into labels. Many emotion fundamentals remain under-explored in natural language processing, particularly how emotions develop and how people cope with them. To help reduce this gap, we follow theories on coping, and treat emotions as strategies to cope with salient situations (i.e., how people deal with emotion-eliciting events). This approach allows us to investigate the link between emotions and behavior, which also emerges in language. We introduce the task of coping identification, together with a corpus to do so, constructed via role-playing. We find that coping strategies realize in text even though they are challenging to recognize, both for humans and automatic systems trained and prompted on the same task. We thus open up a promising research direction to enhance the capability of models to better capture emotion mechanisms from text.

Related papers

• Think out Loud: Emotion Deducing Explanation in Dialogues [57.90554323226896]
  We propose a new task "Emotion Deducing Explanation in Dialogues" (EDEN) EDEN recognizes emotion and causes in an explicitly thinking way. It can help Large Language Models (LLMs) achieve better recognition of emotions and causes.
  arXiv  Detail & Related papers  (2024-06-07T08:58:29Z)
• CauESC: A Causal Aware Model for Emotional Support Conversation [79.4451588204647]
  Existing approaches ignore the emotion causes of the distress. They focus on the seeker's own mental state rather than the emotional dynamics during interaction between speakers. We propose a novel framework CauESC, which firstly recognizes the emotion causes of the distress, as well as the emotion effects triggered by the causes.
  arXiv  Detail & Related papers  (2024-01-31T11:30:24Z)
• Where are We in Event-centric Emotion Analysis? Bridging Emotion Role Labeling and Appraisal-based Approaches [10.736626320566707]
  The term emotion analysis in text subsumes various natural language processing tasks. We argue that emotions and events are related in two ways. We discuss how to incorporate psychological appraisal theories in NLP models to interpret events.
  arXiv  Detail & Related papers  (2023-09-05T09:56:29Z)
• Automatic Emotion Experiencer Recognition [12.447379545167642]
  We show that experiencer detection in text is a challenging task, with a precision of.82 and a recall of.56 (F1 =.66) We show that experiencer detection in text is a challenging task, with a precision of.82 and a recall of.56 (F1 =.66)
  arXiv  Detail & Related papers  (2023-05-26T08:33:28Z)
• Empathetic Response Generation via Emotion Cause Transition Graph [29.418144401849194]
  Empathetic dialogue is a human-like behavior that requires the perception of both affective factors (e.g., emotion status) and cognitive factors (e.g., cause of the emotion) We propose an emotion cause transition graph to explicitly model the natural transition of emotion causes between two adjacent turns in empathetic dialogue. With this graph, the concept words of the emotion causes in the next turn can be predicted and used by a specifically designed concept-aware decoder to generate the empathic response.
  arXiv  Detail & Related papers  (2023-02-23T05:51:17Z)
• Why Do You Feel This Way? Summarizing Triggers of Emotions in Social Media Posts [61.723046082145416]
  We introduce CovidET (Emotions and their Triggers during Covid-19), a dataset of 1,900 English Reddit posts related to COVID-19. We develop strong baselines to jointly detect emotions and summarize emotion triggers. Our analyses show that CovidET presents new challenges in emotion-specific summarization, as well as multi-emotion detection in long social media posts.
  arXiv  Detail & Related papers  (2022-10-22T19:10:26Z)
• Natural Language Processing for Cognitive Analysis of Emotions [0.0]
  We introduce a new annotation scheme for exploring emotions and their causes, along with a new French dataset composed of autobiographical accounts of an emotional scene. The texts were collected by applying the Cognitive Analysis of Emotions developed by A. Finkel to help people improve on their emotion management.
  arXiv  Detail & Related papers  (2022-10-11T09:47:00Z)
• Speech Synthesis with Mixed Emotions [77.05097999561298]
  We propose a novel formulation that measures the relative difference between the speech samples of different emotions. We then incorporate our formulation into a sequence-to-sequence emotional text-to-speech framework. At run-time, we control the model to produce the desired emotion mixture by manually defining an emotion attribute vector.
  arXiv  Detail & Related papers  (2022-08-11T15:45:58Z)
• Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes [50.569762345799354]
  We argue that two issues must be tackled at the same time: (i) identifying which word is the cause for the other's emotion from his or her utterance and (ii) reflecting those specific words in the response generation. Taking inspiration from social cognition, we leverage a generative estimator to infer emotion cause words from utterances with no word-level label.
  arXiv  Detail & Related papers  (2021-09-18T04:22:49Z)
• Emotion Recognition under Consideration of the Emotion Component Process Model [9.595357496779394]
  We use the emotion component process model (CPM) by Scherer (2005) to explain emotion communication. CPM states that emotions are a coordinated process of various subcomponents, in reaction to an event, namely the subjective feeling, the cognitive appraisal, the expression, a physiological bodily reaction, and a motivational action tendency. We find that emotions on Twitter are predominantly expressed by event descriptions or subjective reports of the feeling, while in literature, authors prefer to describe what characters do, and leave the interpretation to the reader.
  arXiv  Detail & Related papers  (2021-07-27T15:53:25Z)
• Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction [55.47134146639492]
  This article proposes a unifed end-to-end neural architecture, which is capable of simultaneously encoding the semantics and the emotions in a post. Experiments on real-world data demonstrate that the proposed method outperforms the state-of-the-art methods in terms of both content coherence and emotion appropriateness.
  arXiv  Detail & Related papers  (2021-06-06T06:26:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.