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

Code

• github.com/michaelfarrell76/End-To-End-Generative-Dialogue
  torch★ 138
• github.com/vamshi009/End-to-End-HRED-Dialogue-System
  pytorch★ 0
• github.com/Tanasho0928/chat-oriented
  pytorch★ 0
• github.com/hsgodhia/hred
  pytorch★ 0
• github.com/wayalhruhi/julianser
  none★ 0
• github.com/Tanasho0928/ncm
  pytorch★ 0
• github.com/julianser/hed-dlg-truncated
  none★ 0

Abstract

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.

Tasks

Reproductions