We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Generative models produce system responses that are autonomously generated word-by-word, opening up the possibility for realistic, flexible interactions. In support of this goal, we extend the recently proposed hierarchical recurrent encoder-decoder neural network to the dialogue domain, and demonstrate that this model is competitive with state-of-the-art neural language models and backoff n-gram models. We investigate the limitations of this and similar approaches, and show how its performance can be improved by bootstrapping the learning from a larger questionanswer pair corpus and from pretrained word embeddings.
An end-to-end dialogue system using generative hierarchical neural network models is presented by Serban et al REF .
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
AAAI 2016
arXiv: Computation and Language
Computer Science