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, and the
performance of the entire system is not robust to the accumulated errors. This
paper presents a novel end-to-end learning framework for task-completion
dialogue systems to tackle such issues. Our neural dialogue system can directly
interact with a structured database to assist users in accessing information
and accomplishing certain tasks. The reinforcement learning based dialogue
manager offers robust capabilities to handle noises caused by other components
of the dialogue system. Our experiments in a movie-ticket booking domain show
that our end-to-end system not only outperforms modularized dialogue system
baselines for both objective and subjective evaluation, but also is robust to
noises as demonstrated by several systematic experiments with different error
granularity and rates specific to the language understanding module.

本文介绍了一种基于强化学习的新型端到端对话系统学习框架，用于解决模块化任务完成功能对话系统在训练和使用中面临的挑战。研究的实验表明，该端到端系统不仅在客观和主观评估中优于传统的模块化对话系统基线，而且在处理语言理解模块的各种误差时具有稳健性。