CoMAE: A Multi-factor Hierarchical Framework for Empathetic Response Generation

Zheng, Chujie; Liu, Yong; Chen, Wei; Leng, Yongcai; Huang, Minlie

doi:10.18653/v1/2021.findings-acl.72

Cited by 40 publications

(23 citation statements)

References 35 publications

(41 reference statements)

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“…Though our human evaluation captures platform users' perceptions of empathy in responses, it is unlikely to measure empathy from the perspective of psychology theory given the limited training of TalkLife users. Therefore, we conducted a second complementary evaluation by applying the theory-based empathy classification model proposed by Sharma et al 29 , which assigns a score between 0 and 6 to each response and has been validated and used in prior work 47,[74][75][76] . Note that this approach evaluates empathy expressed in responses and not the empathy perceived by support seekers of the original seeker post (Discussion).…”

Section: Discussionmentioning

confidence: 99%

Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Support

Sharma¹,

Lin²,

Miner³

et al. 2022

Preprint

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Advances in artificial intelligence (AI) are enabling systems that augment and collaborate with humans to perform simple, mechanistic tasks like scheduling meetings and grammar-checking text. However, such Human-AI collaboration poses challenges for more complex, creative tasks, such as carrying out empathic conversations, due to difficulties of AI systems in understanding complex human emotions and the open-ended nature of these tasks. Here, we focus on peer-to-peer mental health support, a setting in which empathy is critical for success, and examine how AI can collaborate with humans to facilitate peer empathy during textual, online supportive conversations. We develop HAILEY, an AI-in-the-loop agent that provides just-in-time feedback to help participants who provide support (peer supporters) respond more empathically to those seeking help (support seekers). We evaluate HAILEY in a non-clinical randomized controlled trial with real-world peer supporters on TalkLife (N=300), a large online peer-topeer support platform. We show that our Human-AI collaboration approach leads to a 19.60% increase in conversational empathy between peers overall. Furthermore, we find a larger 38.88% increase in empathy within the subsample of peer supporters who self-identify as experiencing difficulty providing support. We systematically analyze the Human-AI collaboration patterns and find that peer supporters are able to use the AI feedback both directly and indirectly without becoming overly reliant on AI while reporting improved self-efficacy post-feedback. Our findings demonstrate the potential of feedback-driven, AI-in-the-loop writing systems to empower humans in open-ended, social, creative tasks such as empathic conversations.

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Section: Discussionmentioning

confidence: 99%

Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Support

Sharma¹,

Lin²,

Miner³

et al. 2022

Preprint

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“…Empathy is a fairly new term in the literature and therefore, has no specific or widely accepted definition in the fields of social psychology and psychotherapy (Macarov 1978;Elliott et al 2011). However, empathy is commonly known as a complex multi-dimensional construct that includes broad aspects of affection and cognition (Davis 1983;Zheng et al 2021). Affective empathy enables us to experience the emotion of others through various emotional stimuli (Cuff et al 2016), while cognitive empathy enables us to understand the situations and implicit mental states of others, such as intentions, causes, desires, requirements, etc.…”

Section: Preliminaries Empathetic Dialogue Generationmentioning

confidence: 99%

CEM: Commonsense-Aware Empathetic Response Generation

Sabour

Zheng

Huang

2022

AAAI

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A key trait of daily conversations between individuals is the ability to express empathy towards others, and exploring ways to implement empathy is a crucial step towards human-like dialogue systems. Previous approaches on this topic mainly focus on detecting and utilizing the user’s emotion for generating empathetic responses. However, since empathy includes both aspects of affection and cognition, we argue that in addition to identifying the user’s emotion, cognitive understanding of the user’s situation should also be considered. To this end, we propose a novel approach for empathetic response generation, which leverages commonsense to draw more information about the user’s situation and uses this additional information to further enhance the empathy expression in generated responses. We evaluate our approach on EMPATHETICDIALOGUES, which is a widely-used benchmark dataset for empathetic response generation. Empirical results demonstrate that our approach outperforms the baseline models in both automatic and human evaluations and can generate more informative and empathetic responses. Our code is available at https://github.com/Sahandfer/CEM.

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“…Conversation generation models can usually be divided into retrieval-based [69], [70] and generative [2], [3], [71]. As shown in Figure 8, generative conversation models can be divided into three types: (1) w/o control signal [2], [72], (2) implicit embedding [3], [73], [74], and (3) explicit fusion [1], [52]. Generally, the latter two architectures are used for personalized conversation generation or emotional conversation generation.…”

Section: Personalized and Emotional Conversationmentioning

confidence: 99%

CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI

Chen¹,

Fan²,

Xing³

et al. 2022

Preprint

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Human language expression is based on the subjective construal of the situation instead of the objective truth conditions, which means that speakers' personalities and emotions after cognitive processing have an important influence on conversation. However, most existing datasets for conversational AI ignore human personalities and emotions, or only consider part of them. It's difficult for dialogue systems to understand speakers' personalities and emotions although large-scale pretraining language models have been widely used. In order to consider both personalities and emotions in the process of conversation generation, we propose CPED, a large-scale Chinese personalized and emotional dialogue dataset, which consists of multi-source knowledge related to empathy and personal characteristic. These knowledge covers gender, Big Five personality traits, 13 emotions, 19 dialogue acts and 10 scenes. CPED contains more than 12K dialogues of 392 speakers from 40 TV shows. We release the textual dataset with audio features and video features according to the copyright claims, privacy issues, terms of service of video platforms. We provide detailed description of the CPED construction process and introduce three tasks for conversational AI, including personality recognition, emotion recognition in conversations as well as personalized and emotional conversation generation. Finally, we provide baseline systems for these tasks and consider the function of speakers' personalities and emotions on conversation. Our motivation is to propose a dataset to be widely adopted by the NLP community as a new open benchmark for conversational AI research. The full dataset is available 1 .

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CoMAE: A Multi-factor Hierarchical Framework for Empathetic Response Generation

Cited by 40 publications

References 35 publications

Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Support

Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Support

CEM: Commonsense-Aware Empathetic Response Generation

CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI

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