ValueNet: A New Dataset for Human Value Driven Dialogue System

Abstract

Building a socially intelligent agent involves many challenges, one of which is to teach the agent to speak guided by its value like a human. However, value-driven chatbots are still understudied in the area of dialogue systems. Most existing datasets focus on commonsense reasoning or social norm modeling. In this work, we present a new large-scale human value dataset called ValueNet, which contains human attitudes on 21,374 text scenarios. The dataset is organized in ten dimensions that conform to the basic human value theory in intercultural research. We further develop a Transformer-based value regression model on ValueNet to learn the utility distribution. Comprehensive empirical results show that the learned value model could benefit a wide range of dialogue tasks. For example, by teaching a generative agent with reinforcement learning and the rewards from the value model, our method attains state-of-the-art performance on the personalized dialog generation dataset: Persona-Chat. With values as additional features, existing emotion recognition models enable capturing rich human emotions in the context, which further improves the empathetic response generation performance in the EmpatheticDialogues dataset. To the best of our knowledge, ValueNet is the first large-scale text dataset for human value modeling, and we are the first one trying to incorporate a value model into emotionally intelligent dialogue systems. The dataset is available at https://liang-qiu.github.io/ValueNet/.

Paper accepted by AAAI 2022

References in corpus (5)

• DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset
• Long Text Generation via Adversarial Training with Leaked Information
• Aligning AI With Shared Human Values
• You Impress Me: Dialogue Generation via Mutual Persona Perception
• SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues

Cited by in corpus (4)

• MoralBERT: A Fine-Tuned Language Model for Capturing Moral Values in Social Discussions
• Multilingual Dyadic Interaction Corpus NoXi+J: Toward Understanding Asian-European Non-verbal Cultural Characteristics and their Influences on Engagement
• Do Differences in Values Influence Disagreements in Online Discussions?
• Learning the Value Systems of Agents with Preference-based and Inverse Reinforcement Learning