Microsoft Dialogue Challenge: Building End-to-End Task-Completion Dialogue Systems

2018-07-29 · Xiujun Li, Yu Wang, Siqi Sun, Sarah Panda, Jingjing Liu, Jianfeng Gao

This proposal introduces a Dialogue Challenge for building end-to-end task-completion dialogue systems, with the goal of encouraging the dialogue research community to collaborate and benchmark on standard datasets and unified experimental environment. In this special session, we will release human-annotated conversational data in three domains (movie-ticket booking, restaurant reservation, and taxi booking), as well as an experiment platform with built-in simulators in each domain, for training and evaluation purposes. The final submitted systems will be evaluated both in simulated setting and by human judges.

📄 PDF Abstract BibTeX arXiv:1807.11125

Code (2)

xiul-msr/e2e_dialog_challenge 공식 구현

ysglh/Task-Oriented-Dialogue-Dataset-Survey

MuLanTTS: The Microsoft Speech Synthesis System for Blizzard Challenge 2023

2023-09-06 · Zhihang Xu, Shaofei Zhang, Xi Wang, Jiajun Zhang 외

In this paper, we present MuLanTTS, the Microsoft end-to-end neural text-to-speech (TTS) system designed for the Blizzard Challenge 2023. About 50 hours of audiobook corpus for French TTS as hub task and another 2 hours …

Speech Synthesis text-to-speech Text to Speech

Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning

2017-04-10 · EMNLP 2017 9 · Baolin Peng, Xiujun Li, Lihong Li, Jianfeng Gao 외

Building a dialogue agent to fulfill complex tasks, such as travel planning, is challenging because the agent has to learn to collectively complete multiple subtasks. For example, the agent needs to reserve a hotel and b…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1

End-to-End Task-Completion Neural Dialogue Systems

2017-03-03 · IJCNLP 2017 11 · Xiujun Li, Yun-Nung Chen, Lihong Li, Jianfeng Gao 외

One of the major drawbacks of modularized task-completion dialogue systems is that each module is trained individually, which presents several challenges. For example, downstream modules are affected by earlier modules, …

Chatbot Reinforcement Learning

Bootstrapping a Neural Conversational Agent with Dialogue Self-Play, Crowdsourcing and On-Line Reinforcement Learning

2018-06-01 · NAACL 2018 6 · Pararth Shah, Dilek Hakkani-T{\"u}r, Bing Liu, Gokhan T{\"u}r

End-to-end neural models show great promise towards building conversational agents that are trained from data and on-line experience using supervised and reinforcement learning. However, these models require a large corp…

Reinforcement Learning Reinforcement Learning (RL)

A User Simulator for Task-Completion Dialogues

2016-12-17 · Xiujun Li, Zachary C. Lipton, Bhuwan Dhingra, Lihong Li 외

Despite widespread interests in reinforcement-learning for task-oriented dialogue systems, several obstacles can frustrate research and development progress. First, reinforcement learners typically require interaction wi…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Task-Oriented Dialogue Systems+1