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

by   Xiujun Li, et al.

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.


End-to-End Task-Completion Neural Dialogue Systems

One of the major drawbacks of modularized task-completion dialogue syste...

Hierarchical Context Enhanced Multi-Domain Dialogue System for Multi-domain Task Completion

Task 1 of the DSTC8-track1 challenge aims to develop an end-to-end multi...

Generating Challenge Datasets for Task-Oriented Conversational Agents through Self-Play

End-to-end neural approaches are becoming increasingly common in convers...

Multi-Task End-to-End Training Improves Conversational Recommendation

In this paper, we analyze the performance of a multitask end-to-end tran...

On the Evaluation of Dialogue Systems with Next Utterance Classification

An open challenge in constructing dialogue systems is developing methods...

Building a Conversational Agent Overnight with Dialogue Self-Play

We propose Machines Talking To Machines (M2M), a framework combining aut...

Manual-Guided Dialogue for Flexible Conversational Agents

How to build and use dialogue data efficiently, and how to deploy models...

Please sign up or login with your details

Forgot password? Click here to reset