End-to-end task dependent recurrent entity network for goal-oriented dialog learning. (January 2019)

• Record Type: Journal Article
• Authors: Shin, Chang-Uk; Cha, Jeong-Won
• Is Part Of: Computer speech & language. Volume 53(2019)
• Page Start: 12
• Page End: 24
• Publication Date: 2019-01
• Subjects: Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2018.06.004
• Languages: English
• ISSNs: 0885-2308

Abstract

Abstract: In this paper, we introduce the Task Dependent Recurrent Entity Network (TDREN) to solve Dialogue System Technology Challenges 6 (DSTC 6) track 1. Traditionally, there have been methods such as collecting the intent of the user in a conversation directly using rules. We design an end-to-end structure that properly models the restaurant pre-related user preferences that appear in the dialogue and gives appropriate responses. We perform experiments on the TDREN and achieved 97.7% at precision 1. We propose a new artificial neural network structure and recurrent cell for modeling user preference information. Then, we show that task-oriented dialogue modeling experiment results using the structure and the recurrent cell.