@inproceedings{gunasekara-etal-2019-dstc7,
title = "{DSTC}7 Task 1: Noetic End-to-End Response Selection",
author = "Gunasekara, Chulaka and
Kummerfeld, Jonathan K. and
Polymenakos, Lazaros and
Lasecki, Walter",
editor = "Chen, Yun-Nung and
Bedrax-Weiss, Tania and
Hakkani-Tur, Dilek and
Kumar, Anuj and
Lewis, Mike and
Luong, Thang-Minh and
Su, Pei-Hao and
Wen, Tsung-Hsien",
booktitle = "Proceedings of the First Workshop on NLP for Conversational AI",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-4107",
doi = "10.18653/v1/W19-4107",
pages = "60--67",
abstract = "Goal-oriented dialogue in complex domains is an extremely challenging problem and there are relatively few datasets. This task provided two new resources that presented different challenges: one was focused but small, while the other was large but diverse. We also considered several new variations on the next utterance selection problem: (1) increasing the number of candidates, (2) including paraphrases, and (3) not including a correct option in the candidate set. Twenty teams participated, developing a range of neural network models, including some that successfully incorporated external data to boost performance. Both datasets have been publicly released, enabling future work to build on these results, working towards robust goal-oriented dialogue systems.",
}
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<abstract>Goal-oriented dialogue in complex domains is an extremely challenging problem and there are relatively few datasets. This task provided two new resources that presented different challenges: one was focused but small, while the other was large but diverse. We also considered several new variations on the next utterance selection problem: (1) increasing the number of candidates, (2) including paraphrases, and (3) not including a correct option in the candidate set. Twenty teams participated, developing a range of neural network models, including some that successfully incorporated external data to boost performance. Both datasets have been publicly released, enabling future work to build on these results, working towards robust goal-oriented dialogue systems.</abstract>
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%0 Conference Proceedings
%T DSTC7 Task 1: Noetic End-to-End Response Selection
%A Gunasekara, Chulaka
%A Kummerfeld, Jonathan K.
%A Polymenakos, Lazaros
%A Lasecki, Walter
%Y Chen, Yun-Nung
%Y Bedrax-Weiss, Tania
%Y Hakkani-Tur, Dilek
%Y Kumar, Anuj
%Y Lewis, Mike
%Y Luong, Thang-Minh
%Y Su, Pei-Hao
%Y Wen, Tsung-Hsien
%S Proceedings of the First Workshop on NLP for Conversational AI
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F gunasekara-etal-2019-dstc7
%X Goal-oriented dialogue in complex domains is an extremely challenging problem and there are relatively few datasets. This task provided two new resources that presented different challenges: one was focused but small, while the other was large but diverse. We also considered several new variations on the next utterance selection problem: (1) increasing the number of candidates, (2) including paraphrases, and (3) not including a correct option in the candidate set. Twenty teams participated, developing a range of neural network models, including some that successfully incorporated external data to boost performance. Both datasets have been publicly released, enabling future work to build on these results, working towards robust goal-oriented dialogue systems.
%R 10.18653/v1/W19-4107
%U https://aclanthology.org/W19-4107
%U https://doi.org/10.18653/v1/W19-4107
%P 60-67
Markdown (Informal)
[DSTC7 Task 1: Noetic End-to-End Response Selection](https://aclanthology.org/W19-4107) (Gunasekara et al., ACL 2019)
ACL
- Chulaka Gunasekara, Jonathan K. Kummerfeld, Lazaros Polymenakos, and Walter Lasecki. 2019. DSTC7 Task 1: Noetic End-to-End Response Selection. In Proceedings of the First Workshop on NLP for Conversational AI, pages 60–67, Florence, Italy. Association for Computational Linguistics.