Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
Iulian Vlad Serban, Alessandro Sordoni, Yoshua Bengio, Aaron C. Courville, Joëlle Pineau · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · 2016
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 back-off 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 question-answer pair corpus and from pretrained word embeddings.