@inproceedings{eshghi-etal-2017-bootstrapping,
title = "Bootstrapping incremental dialogue systems from minimal data: the generalisation power of dialogue grammars",
author = "Eshghi, Arash and
Shalyminov, Igor and
Lemon, Oliver",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1236",
doi = "10.18653/v1/D17-1236",
pages = "2220--2230",
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, the MEMN2N model shows very poor robustness to the bAbI+ transformations even when trained on the full bAbI dataset.",
}
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%0 Conference Proceedings
%T Bootstrapping incremental dialogue systems from minimal data: the generalisation power of dialogue grammars
%A Eshghi, Arash
%A Shalyminov, Igor
%A Lemon, Oliver
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F eshghi-etal-2017-bootstrapping
%X 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, the MEMN2N model shows very poor robustness to the bAbI+ transformations even when trained on the full bAbI dataset.
%R 10.18653/v1/D17-1236
%U https://aclanthology.org/D17-1236
%U https://doi.org/10.18653/v1/D17-1236
%P 2220-2230
Markdown (Informal)
[Bootstrapping incremental dialogue systems from minimal data: the generalisation power of dialogue grammars](https://aclanthology.org/D17-1236) (Eshghi et al., EMNLP 2017)
ACL