Training Neural Response Selection for Task-Oriented Dialogue Systems

Matthew Henderson; Ivan Vulić; Daniela Gerz; Iñigo Casanueva; Paweł Budzianowski; Sam Coope; Georgios Spithourakis; Tsung-Hsien Wen; Nikola Mrkšić; Pei-Hao Su

arXiv · 1906.01543

Training Neural Response Selection for Task-Oriented Dialogue Systems

Matthew Henderson·Ivan Vulić·Daniela Gerz·Iñigo Casanueva·Paweł Budzianowski·Sam Coope·Georgios Spithourakis·Tsung-Hsien Wen·Nikola Mrkšić·Pei-Hao Su

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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 six diverse application domains, ranging from e-commerce to banking, demonstrates the effectiveness of the proposed training method.

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Matthew Henderson, Ivan Vulić, Daniela Gerz, Iñigo Casanueva, Paweł Budzianowski, Sam Coope, Georgios Spithourakis, Tsung-Hsien Wen, Nikola Mrkšić, Pei-Hao Su. 2019-06-04. Training Neural Response Selection for Task-Oriented Dialogue Systems. https://arxiv.org/abs/1906.01543

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