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
Venues:
ACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
60–67
Language:
URL:
https://www.aclweb.org/anthology/W19-4107
DOI:
10.18653/v1/W19-4107
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PDF:
http://aclanthology.lst.uni-saarland.de/W19-4107.pdf