%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