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2019 ACL ACL 2019

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

Abstract

AbstractGoal-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.

🌉 Interdisciplinary Bridge — Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — response selection
🐝 Cross-Pollinator — Artificial Intelligence, Computer Vision, Deep Learning, Interdisciplinary, Machine Learning, Natural Language Processing, Reinforcement Learning, Speech & Audio
🐣 Hot Topic Early Bird — dialogue state tracking

Authors

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

Topics

Machine Learning > Core Methods > Classification Natural Language Processing > Generation > Dialogue Systems Natural Language Processing > Applications > Intent Classification Natural Language Processing > Applications > Dialogue Systems Machine Learning > Learning Types > Deep Learning Deep Learning > Learning Types > Multi-Modal Learning

Keywords

dialogue state tracking neural network model goal-oriented dialogue dialogue system response selection utterance selection paraphrase inclusion neural network next utterance selection
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