A Taxonomy of Empathetic Response Intents in Human Social Conversations

Welivita, Anuradha; Pu, Pearl

doi:10.18653/v1/2020.coling-main.429

Cited by 63 publications

(58 citation statements)

References 37 publications

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“…Recent years have witnessed a boom of research on data-driven analysis and application of empathy in general conversations. In terms of empathy analysis for open-domain conversations, Zhou et al (2021) addressed scoring empathy grounded in specific situations, Welivita and Pu (2020) created a taxonomy of empathetic response intents in social dialogues, while Guda et al (2021) proposed to take user demographic information into account for empathy prediction.…”

Section: Data-driven Text-based Research On Empathy In General Conversationmentioning

confidence: 99%

Towards Low-Resource Real-Time Assessment of Empathy in Counselling

Wu¹,

Helaoui²,

Recupero³

et al. 2021

Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access

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Gauging therapist empathy in counselling is an important component of understanding counselling quality. While session-level empathy assessment based on machine learning has been investigated extensively, it relies on relatively large amounts of well-annotated dialogue data, and real-time evaluation has been overlooked in the past. In this paper, we focus on the task of low-resource utterance-level binary empathy assessment. We train deep learning models on heuristically constructed empathy vs. non-empathy contrast in general conversations, and apply the models directly to therapeutic dialogues, assuming correlation between empathy manifested in those two domains. We show that such training yields poor performance in general, probe its causes, and examine the actual effect of learning from empathy contrast in general conversation. r/offmychest ... Speaker: being married to a depressed person is so lonely . that is all . thanks for listening . Listener: sorry to say this but it 's not worth being in a relationship if both of you are n't happy . r/CasualConversation ... Speaker: can you recommend me some good music ? i 've decided to expand my taste in music and i need some advice . what are your favourite songs , redditors ? Listener: something corporate , jimmy eat world , and fall out boy are my top three recommendations . RolePlayMI ... Client: well I guess if I don't want to take more pills I have to give up some of my sweets my cookies and my potato chips Therapist: so those types of sweets and crunchy stuff and salty stuff is is pretty important to you Ground Truth: Empathetic RolePlayMI ... Client: I do all of the above yeah Therapist: So you know the smoking is going be related to not just to some of the other negative consequences you know physically it it effects dental carries, dental cavities

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Section: Data-driven Text-based Research On Empathy In General Conversationmentioning

confidence: 99%

Towards Low-Resource Real-Time Assessment of Empathy in Counselling

Wu¹,

Helaoui²,

Recupero³

et al. 2021

Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access

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“…(Neutral: Sympathizing) Table 1: An example showing the listener's reactions to emotions do not always mirror the speaker's emotions. Welivita and Pu (2020) have analyzed listener responses in the EmpatheticDialogues dataset (Rashkin et al, 2018) and discovered eight listener specific empathetic response intents contained in emotional dialogues: Questioning; Agreeing; Acknowledging; Sympathizing; Encouraging; Consoling: Suggesting; and Wishing. They have annotated the EmpatheticDialogues dataset with 32 fine-grained emotions, eight empathetic response intents, and the Neutral category, and discovered frequent emotion-intent exchange patterns in empathetic conversations.…”

Section: Speakermentioning

confidence: 99%

“…The 32 emotions range from basic emotions derived from biological responses (Ekman, 1992;Plutchik, 1984) to larger sets of subtle emotions derived from contextual situations (Skerry and Saxe, 2015). Welivita and Pu (2020) manually analyzed a subset of the listener turns in EmpatheticDialogues and identified eight listener-specific response intents. They developed a sentence-level weak labeler using which they annotated the entire dataset with 32 emotions, eight empathetic response intents, and the Neutral category.…”

Section: Speakermentioning

confidence: 99%

“…To filter out dialogues containing emotional statements and empathetic responses from the cleaned OS dialogues dataset, we employed a weak labeler, (a BERT transformer-based sentence level classifier) trained on 25K situation descriptions from EmpatheticDialogues (Rashkin et al, 2018) tagged with 32 emotion classes, and 7K listener utterances tagged with eight empathetic response intents and the Neutral category (Welivita and Pu, 2020). The classifier had a high top-1 classification accuracy of 65.88%.…”

Section: Dialogue Curation From Movie Subtitlesmentioning

confidence: 99%

“…By observing the examples, it could be noticed that even for less confident predictions, the label quite accurately describes the emotion or intent of the corresponding dialogue turn. We also conducted a qualitative comparison of the annotations in the EDOS dataset with Empa-theticDialogues (Rashkin et al, 2018;Welivita and Pu, 2020), a state-of-the-art gold standard dataset for empathetic conversations. Figure 2 compares the distributions of emotions and intents in the two datasets.…”

Section: Edos Quality Analysis and Comparisonmentioning

confidence: 99%

See 2 more Smart Citations

A Large-Scale Dataset for Empathetic Response Generation

Welivita¹,

Xie²,

Pu³

2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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Recent development in NLP shows a strong trend towards refining pre-trained models with a domain-specific dataset. This is especially the case for response generation where emotion plays an important role. However, existing empathetic datasets remain small, delaying research efforts in this area, for example, the development of emotion-aware chatbots. One main technical challenge has been the cost of manually annotating dialogues with the right emotion labels. In this paper, we describe a large-scale silver dataset consisting of 1M dialogues annotated with 32 fine-grained emotions, eight empathetic response intents, and the Neutral category. To achieve this goal, we have developed a novel data curation pipeline starting with a small seed of manually annotated data and eventually scaling it to a satisfactory size. We compare its quality against a state-of-the-art gold dataset using offline experiments and visual validation methods. The resultant procedure can be used to create similar datasets in the same domain as well as in other domains. 1

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Using Extracted Emotion Cause to Improve Content-Relevance for Empathetic Conversation Generation

Zou

Pan

Zhang

et al. 2022

Lecture Notes in Computer Science

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Empathetic conversation generation intends to endow the open-domain conversation model with the capability for understanding, interpreting, and expressing emotion. Humans express not only their emotional state but also the stimulus that caused the emotion, i.e., emotion cause, during a conversation. Most existing approaches focus on emotion modeling, emotion recognition and prediction, and emotion fusion generation, ignoring the critical aspect of the emotion cause, which results in generating responses with irrelevant content. Emotion cause can help the model understand the user's emotion and make the generated responses more content-relevant. However, using the emotion cause to enhance empathetic conversation generation is challenging. Firstly, the model needs to accurately identify the emotion cause without large-scale labeled data. Second, the model needs to effectively integrate the emotion cause into the generation process. To this end, we present an emotion cause extractor using a semi-supervised training method and an empathetic conversation generator using a biased self-attention mechanism to overcome these two issues. Experimental results indicate that our proposed emotion cause extractor improves recall scores markedly compared to the baselines, and the proposed empathetic conversation generator has superior performance and improves the content-relevance of generated responses.

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A Taxonomy of Empathetic Response Intents in Human Social Conversations

Cited by 63 publications

References 37 publications

Towards Low-Resource Real-Time Assessment of Empathy in Counselling

Towards Low-Resource Real-Time Assessment of Empathy in Counselling

A Large-Scale Dataset for Empathetic Response Generation

Using Extracted Emotion Cause to Improve Content-Relevance for Empathetic Conversation Generation

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