Transformer-based models have demonstrated excellent capabilities of
capturing patterns and structures in natural language generation and achieved
state-of-the-art results in many tasks. In this paper we present a
transformer-based model for multi-turn dialog response generation. Our solution
is based on a hybrid approach which augments a transformer-based generative
model with a novel retrieval mechanism, which leverages the memorized
information in the training data via k-Nearest Neighbor search. Our system is
evaluated on two datasets made by customer/assistant dialogs: the Taskmaster-1,
released by Google and holding high quality, goal-oriented conversational data
and a proprietary dataset collected from a real customer service call center.
Both achieve better BLEU scores over strong baselines.

本研究提出了基于 Transformer 的多轮对话响应生成模型，采用新颖的检索机制，结合 k 最近邻搜索，基于 Google 发布的 Taskmaster-1 数据集和真实的客户服务呼叫中心数据集进行评估，结果优于强基线模型。

检索增强的 Transformer-XL 用于闭域对话生成

Retrieval-Augmented Transformer-XL for Close-Domain Dialog Generation

A significant barrier to progress in data-driven approaches to building
dialog systems is the lack of high quality, goal-oriented conversational data.
To help satisfy this elementary requirement, we introduce the initial release
of the Taskmaster-1 dataset which includes 13,215 task-based dialogs comprising
six domains. Two procedures were used to create this collection, each with
unique advantages. The first involves a two-person, spoken "Wizard of Oz" (WOz)
approach in which trained agents and crowdsourced workers interact to complete
the task while the second is "self-dialog" in which crowdsourced workers write
the entire dialog themselves. We do not restrict the workers to detailed
scripts or to a small knowledge base and hence we observe that our dataset
contains more realistic and diverse conversations in comparison to existing
datasets. We offer several baseline models including state of the art neural
seq2seq architectures with benchmark performance as well as qualitative human
evaluations. Dialogs are labeled with API calls and arguments, a simple and
cost effective approach which avoids the requirement of complex annotation
schema. The layer of abstraction between the dialog model and the service
provider API allows for a given model to interact with multiple services that
provide similar functionally. Finally, the dataset will evoke interest in
written vs. spoken language, discourse patterns, error handling and other
linguistic phenomena related to dialog system research, development and design.

该研究介绍了一个新的数据集 Taskmaster-1，其中包括 13,215 个基于任务的对话，包括六个领域。数据集包含了更多现实和多样化的对话，使用了两种不同的方法进行数据收集并提供了多个基准模型，其中 API 调用和参数被标记为基于任务的对话系统的研究、开发和设计提供了新的前景。