Adaptive Multi-Domain Dialogue State Tracking on Spoken Conversations
Author:
Affiliation:
1. Computer Science and Engineering, Korea University, Seongbuk-gu, South Korea
2. Naver Corp, Seongnam, South Korea
3. Korea University, Seongbuk-gu, South Korea
Funder
Ministry of Science and ICT, South Korea
Information Technology Research Center
Institute for Information & Communications Technology Planning & Evaluation
Institute of Information & Communications Technology Planning & Evaluation
Korea Government
ICT
Institute for Information & communications Technology Planning & Evaluation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Acoustics and Ultrasonics,Computer Science (miscellaneous),Computational Mathematics
Link
http://xplorestaging.ieee.org/ielx7/6570655/10304349/10210521.pdf?arnumber=10210521
Reference27 articles.
1. Frames: a corpus for adding memory to goal-oriented dialogue systems
2. Building a conversational agent overnight with dialogue self-play;Shah,2018
3. A Network-based End-to-End Trainable Task-oriented Dialogue System
4. MultiWOZ 2.1: A consolidated multi-domain dialogue dataset with state corrections and state tracking baselines;Eric,2020
5. MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems
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