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Paper Detail

Presentation #	5
Session:	Dialogue
Session Time:	Thursday, December 20, 10:00 - 12:00
Presentation Time:	Thursday, December 20, 10:00 - 12:00
Presentation:	Poster
Topic:	Spoken dialog systems:
Paper Title:	CONTEXT-AWARE DIALOG RE-RANKING FOR TASK-ORIENTED DIALOG SYSTEMS
Authors:	Junki Ohmura; Sony Corporation
	Maxine Eskenazi; Language Technologies Institute Carnegie Mellon University
Abstract:	Dialog response ranking is used to rank response candidates by considering their relation to the dialog history. Although researchers have addressed this concept for open-domain dialogs, little attention has been focused on task-oriented dialogs. Furthermore, no previous studies have analyzed whether response ranking can improve the performance of existing dialog systems in real human--computer dialogs with speech recognition errors. In this paper, we propose a context-aware dialog response re-ranking system. Our system reranks responses in two steps: (1) it calculates matching scores for each candidate response and the current dialog context; (2) it combines the matching scores and a probability distribution of the candidates from an existing dialog system for response re-ranking. By using neural word embedding-based models and handcrafted or logistic regression-based ensemble models, we have improved the performance of a recently proposed end-to-end task-oriented dialog system on real dialogs with speech recognition errors.