Name
Title | ||
|---|---|---|
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models |
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 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 question-answer pair corpus and from pretrained word embeddings. |
Year | Venue | Field |
|---|---|---|
2016 | AAAI'16 Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence | Hierarchical neural network,End-to-end principle,Computer science,Bootstrapping,Artificial intelligence,Natural language processing,Generative grammar,Artificial neural network,Machine learning,Language model,Generative Design |
DocType | Citations | PageRank |
Conference | 245 | 6.56 |
References | Authors | |
33 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Iulian Vlad Serban | 1 | 854 | 30.73 |
| Alessandro Sordoni | 2 | 801 | 38.18 |
| Yoshua Bengio | 3 | 42677 | 3039.83 |
| Aaron C. Courville | 4 | 6671 | 348.46 |
| Joelle Pineau | 5 | 2857 | 184.18 |