Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models

Understand

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.