3 research outputs found
Retrieval-based Goal-Oriented Dialogue Generation
Most research on dialogue has focused either on dialogue generation for
openended chit chat or on state tracking for goal-directed dialogue. In this
work, we explore a hybrid approach to goal-oriented dialogue generation that
combines retrieval from past history with a hierarchical, neural
encoder-decoder architecture. We evaluate this approach in the customer support
domain using the Multiwoz dataset (Budzianowski et al., 2018). We show that
adding this retrieval step to a hierarchical, neural encoder-decoder
architecture leads to significant improvements, including responses that are
rated more appropriate and fluent by human evaluators. Finally, we compare our
retrieval-based model to various semantically conditioned models explicitly
using past dialog act information, and find that our proposed model is
competitive with the current state of the art (Chen et al., 2019), while not
requiring explicit labels about past machine acts