  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/.
