We present our submission to the End-to-End Multi-Domain Dialog Challenge
Track of the Eighth Dialog System Technology Challenge. Our proposed dialog
system adopts a pipeline architecture, with distinct components for Natural
Language Understanding, Dialog State Tracking, Dialog Management and Natural
Language Generation. At the core of our system is a reinforcement learning
algorithm which uses Deep Q-learning from Demonstrations to learn a dialog
policy with the help of expert examples. We find that demonstrations are
essential to training an accurate dialog policy where both state and action
spaces are large. Evaluation of our Dialog Management component shows that our
approach is effective - beating supervised and reinforcement learning
baselines.

我们对第八次对话系统技术挑战赛的端到端多域对话跟踪进行了提交。我们的系统采用管道架构，包含自然语言理解、对话状态跟踪、对话管理和自然语言生成等组件。我们利用基于示范的深度 Q 学习强化学习算法来学习对话策略，并通过对话管理组件的评估表明该方法的有效性优于监督和强化学习基线模型。

指引我们：从演示中学习对话管理

Show Us the Way: Learning to Manage Dialog from Demonstrations

Traditionally, most data-to-text applications have been designed using a
modular pipeline architecture, in which non-linguistic input data is converted
into natural language through several intermediate transformations. In
contrast, recent neural models for data-to-text generation have been proposed
as end-to-end approaches, where the non-linguistic input is rendered in natural
language with much less explicit intermediate representations in-between. This
study introduces a systematic comparison between neural pipeline and end-to-end
data-to-text approaches for the generation of text from RDF triples. Both
architectures were implemented making use of state-of-the art deep learning
methods as the encoder-decoder Gated-Recurrent Units (GRU) and Transformer.
Automatic and human evaluations together with a qualitative analysis suggest
that having explicit intermediate steps in the generation process results in
better texts than the ones generated by end-to-end approaches. Moreover, the
pipeline models generalize better to unseen inputs. Data and code are publicly
available.

本研究比较了基于神经网络的数据到文本生成的传统管道架构和端到端方法，研究结果表明基于管道架构的模型在生成过程中明确的中间步骤有助于生成更好的文本，且模型具有更好的泛化性。