Paper Info
Title
Bootstrapping incremental dialogue systems from minimal data: the generalisation power of dialogue grammars.
Abstract
We investigate an end-to-end method for automatically inducing task-based dialogue systems from small amounts of unannotated dialogue data. It combines an incremental semantic grammar - Dynamic Syntax and Type Theory with Records (DS-TTR) - with Reinforcement Learning (RL), where language generation and dialogue management are a joint decision problem. The systems thus produced are incremental: dialogues are processed word-by-word, shown previously to be essential in supporting natural, spontaneous dialogue. We hypothesised that the rich linguistic knowledge within the grammar should enable a combinatorially large number of dialogue variations to be processed, even when trained on very few dialogues. Our experiments show that our model can process 74% of the Facebook AI bAbI dataset even when trained on only 0.13% of the data (5 dialogues). It can in addition process 65% of bAbI+, a corpus we created by systematically adding incremental dialogue phenomena such as restarts and self-corrections to bAbI. We compare our model with a state-of-the-art retrieval model, MemN2N. We find that, in terms of semantic accuracy, MemN2N shows very poor robustness to the bAbI+ transformations even when trained on the full bAbI dataset.
Year
DOI
Venue
2017
10.18653/v1/d17-1236
empirical methods in natural language processing
DocType
Volume
ISSN
Journal
abs/1709.07858
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (ISBN 978-1-945626-83-8), pp 2210-2220. Copenhagen, Denmark September 7-11, 2017
Citations 
PageRank 
References 
2
0.37
7
Authors
3
Name
Order
Citations
PageRank
Arash Eshghi1187.61
Igor Shalyminov293.30
Oliver Lemon313514.94