DSTC7 Task 1: Noetic End-to-End Response Selection

Chulaka Gunasekara, Jonathan K. Kummerfeld, Lazaros Polymenakos, Walter Lasecki

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.
Anthology ID:
W19-4107
Volume:
Proceedings of the First Workshop on NLP for Conversational AI
Month:
August
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
60–67
Language:
URL:
https://aclanthology.org/W19-4107
DOI:
10.18653/v1/W19-4107
Bibkey:
Cite (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.
Cite (Informal):
DSTC7 Task 1: Noetic End-to-End Response Selection (Gunasekara et al., ACL 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/W19-4107.pdf
Data
Advising Corpus, DSTC7 Task 1
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