5 research outputs found
Low-Resource Response Generation with Template Prior
We study open domain response generation with limited message-response pairs.
The problem exists in real-world applications but is less explored by the
existing work. Since the paired data now is no longer enough to train a neural
generation model, we consider leveraging the large scale of unpaired data that
are much easier to obtain, and propose response generation with both paired and
unpaired data. The generation model is defined by an encoder-decoder
architecture with templates as prior, where the templates are estimated from
the unpaired data as a neural hidden semi-markov model. By this means, response
generation learned from the small paired data can be aided by the semantic and
syntactic knowledge in the large unpaired data. To balance the effect of the
prior and the input message to response generation, we propose learning the
whole generation model with an adversarial approach. Empirical studies on
question response generation and sentiment response generation indicate that
when only a few pairs are available, our model can significantly outperform
several state-of-the-art response generation models in terms of both automatic
and human evaluation.Comment: Accepted by EMNLP201
Generating Multiple Diverse Responses for Short-Text Conversation
Neural generative models have become popular and achieved promising
performance on short-text conversation tasks. They are generally trained to
build a 1-to-1 mapping from the input post to its output response. However, a
given post is often associated with multiple replies simultaneously in real
applications. Previous research on this task mainly focuses on improving the
relevance and informativeness of the top one generated response for each post.
Very few works study generating multiple accurate and diverse responses for the
same post. In this paper, we propose a novel response generation model, which
considers a set of responses jointly and generates multiple diverse responses
simultaneously. A reinforcement learning algorithm is designed to solve our
model. Experiments on two short-text conversation tasks validate that the
multiple responses generated by our model obtain higher quality and larger
diversity compared with various state-of-the-art generative models.Comment: Accepted for publication at AAAI 201