Dialogue systems benefit greatly from optimizing on detailed annotations,
such as transcribed utterances, internal dialogue state representations and
dialogue act labels. However, collecting these annotations is expensive and
time-consuming, holding back development in the area of dialogue modelling. In
this paper, we investigate semi-supervised learning methods that are able to
reduce the amount of required intermediate labelling. We find that by
leveraging un-annotated data instead, the amount of turn-level annotations of
dialogue state can be significantly reduced when building a neural dialogue
system. Our analysis on the MultiWOZ corpus, covering a range of domains and
topics, finds that annotations can be reduced by up to 30\% while maintaining
equivalent system performance. We also describe and evaluate the first
end-to-end dialogue model created for the MultiWOZ corpus.

本文研究了利用半监督学习方法在构建神经对话系统时可以减少中间标签的数量，发现可以利用未注释数据来显著减少对话状态的转换级别注释，同时在 MultiWOZ 语料库上进行了分析并提出了第一个端到端的对话模型。

面向任务建模的对话状态跟踪的半监督引导

Semi-supervised Bootstrapping of Dialogue State Trackers for Task  Oriented Modelling

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。