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Found 8 papers, 6 papers with code
Curating a Large-Scale Motivational Interviewing Dataset Using Peer Support Forums
We use this data to conclude the extent to which conversational data from peer support platforms align with real therapeutic conversations and discuss in what ways they can be exploited to train therapeutic chatbots.
A Taxonomy of Empathetic Questions in Social Dialogs
To address this gap, we have developed an empathetic question taxonomy (EQT), with special attention paid to questions’ ability to capture communicative acts and their emotion-regulation intents.
A Large-Scale Dataset for Empathetic Response Generation
Recent development in NLP shows a strong trend towards refining pre-trained models with a domain-specific dataset.
Is ChatGPT More Empathetic than Humans?
This paper investigates the empathetic responding capabilities of ChatGPT, particularly its latest iteration, GPT-4, in comparison to human-generated responses to a wide range of emotional scenarios, both positive and negative.
Use of a Taxonomy of Empathetic Response Intents to Control and Interpret Empathy in Neural Chatbots
In this work, we make use of a taxonomy of eight empathetic response intents in addition to generic emotion categories in building a dialogue response generation model capable of generating empathetic responses in a controllable and interpretable manner.
Boosting Distress Support Dialogue Responses with Motivational Interviewing Strategy
AI-driven chatbots have become an emerging solution to address psychological distress.
Fine-grained Emotion and Intent Learning in Movie Dialogues
We propose a novel large-scale emotional dialogue dataset, consisting of 1M dialogues retrieved from the OpenSubtitles corpus and annotated with 32 emotions and 9 empathetic response intents using a BERT-based fine-grained dialogue emotion classifier.
A Taxonomy of Empathetic Response Intents in Human Social Conversations
Our goal is to produce a large-scale taxonomy for empathetic response intents.
