@inproceedings{henderson-etal-2019-training,
title = "Training Neural Response Selection for Task-Oriented Dialogue Systems",
author = "Henderson, Matthew and
Vuli{\'c}, Ivan and
Gerz, Daniela and
Casanueva, I{\~n}igo and
Budzianowski, Pawe{\l} and
Coope, Sam and
Spithourakis, Georgios and
Wen, Tsung-Hsien and
Mrk{\v{s}}i{\'c}, Nikola and
Su, Pei-Hao",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1536",
doi = "10.18653/v1/P19-1536",
pages = "5392--5404",
abstract = "Despite their popularity in the chatbot literature, retrieval-based models have had modest impact on task-oriented dialogue systems, with the main obstacle to their application being the low-data regime of most task-oriented dialogue tasks. Inspired by the recent success of pretraining in language modelling, we propose an effective method for deploying response selection in task-oriented dialogue. To train response selection models for task-oriented dialogue tasks, we propose a novel method which: 1) pretrains the response selection model on large general-domain conversational corpora; and then 2) fine-tunes the pretrained model for the target dialogue domain, relying only on the small in-domain dataset to capture the nuances of the given dialogue domain. Our evaluation on five diverse application domains, ranging from e-commerce to banking, demonstrates the effectiveness of the proposed training method.",
}
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<abstract>Despite their popularity in the chatbot literature, retrieval-based models have had modest impact on task-oriented dialogue systems, with the main obstacle to their application being the low-data regime of most task-oriented dialogue tasks. Inspired by the recent success of pretraining in language modelling, we propose an effective method for deploying response selection in task-oriented dialogue. To train response selection models for task-oriented dialogue tasks, we propose a novel method which: 1) pretrains the response selection model on large general-domain conversational corpora; and then 2) fine-tunes the pretrained model for the target dialogue domain, relying only on the small in-domain dataset to capture the nuances of the given dialogue domain. Our evaluation on five diverse application domains, ranging from e-commerce to banking, demonstrates the effectiveness of the proposed training method.</abstract>
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%0 Conference Proceedings
%T Training Neural Response Selection for Task-Oriented Dialogue Systems
%A Henderson, Matthew
%A Vulić, Ivan
%A Gerz, Daniela
%A Casanueva, Iñigo
%A Budzianowski, Paweł
%A Coope, Sam
%A Spithourakis, Georgios
%A Wen, Tsung-Hsien
%A Mrkšić, Nikola
%A Su, Pei-Hao
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F henderson-etal-2019-training
%X Despite their popularity in the chatbot literature, retrieval-based models have had modest impact on task-oriented dialogue systems, with the main obstacle to their application being the low-data regime of most task-oriented dialogue tasks. Inspired by the recent success of pretraining in language modelling, we propose an effective method for deploying response selection in task-oriented dialogue. To train response selection models for task-oriented dialogue tasks, we propose a novel method which: 1) pretrains the response selection model on large general-domain conversational corpora; and then 2) fine-tunes the pretrained model for the target dialogue domain, relying only on the small in-domain dataset to capture the nuances of the given dialogue domain. Our evaluation on five diverse application domains, ranging from e-commerce to banking, demonstrates the effectiveness of the proposed training method.
%R 10.18653/v1/P19-1536
%U https://aclanthology.org/P19-1536
%U https://doi.org/10.18653/v1/P19-1536
%P 5392-5404
Markdown (Informal)
[Training Neural Response Selection for Task-Oriented Dialogue Systems](https://aclanthology.org/P19-1536) (Henderson et al., ACL 2019)
ACL
- Matthew Henderson, Ivan Vulić, Daniela Gerz, Iñigo Casanueva, Paweł Budzianowski, Sam Coope, Georgios Spithourakis, Tsung-Hsien Wen, Nikola Mrkšić, and Pei-Hao Su. 2019. Training Neural Response Selection for Task-Oriented Dialogue Systems. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5392–5404, Florence, Italy. Association for Computational Linguistics.