This paper summarizes our work on the first track of the ninth Dialog System
Technology Challenge (DSTC 9), "Beyond Domain APIs: Task-oriented
Conversational Modeling with Unstructured Knowledge Access". The goal of the
task is to generate responses to user turns in a task-oriented dialog that
require knowledge from unstructured documents. The task is divided into three
subtasks: detection, selection and generation. In order to be compute
efficient, we formulate the selection problem in terms of hierarchical
classification steps. We achieve our best results with this model.
Alternatively, we employ siamese sequence embedding models, referred to as
Dense Knowledge Retrieval, to retrieve relevant documents. This method further
reduces the computation time by a factor of more than 100x at the cost of
degradation in R@1 of 5-6% compared to the first model. Then for either
approach, we use Retrieval Augmented Generation to generate responses based on
multiple selected snippets and we show how the method can be used to fine-tune
trained embeddings.

本文总结了我们在第九届对话系统技术挑战赛（DSTC 9）第一轨中的工作，我们提出了一种基于层次分类和序列嵌入的检索和生成方法，以实现针对任务导向对话中利用非结构化文本信息的回答生成。

面向任务的对话中基于非结构化知识的高效检索增强生成

Efficient Retrieval Augmented Generation from Unstructured Knowledge for  Task-Oriented Dialog

Task-oriented conversational modeling with unstructured knowledge access, as
track 1 of the 9th Dialogue System Technology Challenges (DSTC 9), requests to
build a system to generate response given dialogue history and knowledge
access. This challenge can be separated into three subtasks, (1)
knowledge-seeking turn detection, (2) knowledge selection, and (3)
knowledge-grounded response generation. We use pre-trained language models,
ELECTRA and RoBERTa, as our base encoder for different subtasks. For subtask 1
and 2, the coarse-grained information like domain and entity are used to
enhance knowledge usage. For subtask 3, we use a latent variable to encode
dialog history and selected knowledge better and generate responses combined
with copy mechanism. Meanwhile, some useful post-processing strategies are
performed on the model's final output to make further knowledge usage in the
generation task. As shown in released evaluation results, our proposed system
ranks second under objective metrics and ranks fourth under human metrics.

本文描述利用预训练语言模型及粗层次知识信息进行基于任务的对话建模，实现对话历史和知识访问的响应生成，并在模型最终输出的基础上进行后处理策略以进一步提升知识应用，在客观指标下获得第二名，在人类指标下获得第四名的实验结果。