Constructing responses in task-oriented dialogue systems typically relies on
information sources such the current dialogue state or external databases. This
paper presents a novel approach to knowledge-grounded response generation that
combines retrieval-augmented language models with logical reasoning. The
approach revolves around a knowledge graph representing the current dialogue
state and background information, and proceeds in three steps. The knowledge
graph is first enriched with logically derived facts inferred using
probabilistic logical programming. A neural model is then employed at each turn
to score the conversational relevance of each node and edge of this extended
graph. Finally, the elements with highest relevance scores are converted to a
natural language form, and are integrated into the prompt for the neural
conversational model employed to generate the system response.
We investigate the benefits of the proposed approach on two datasets (KVRET
and GraphWOZ) along with a human evaluation. Experimental results show that the
combination of (probabilistic) logical reasoning with conversational relevance
scoring does increase both the factuality and fluency of the responses.

使用概率逻辑编程推演引入逻辑事实，结合基于检索的语言模型和逻辑推理的知识生成响应的新方法，在任务导向的对话系统中提高了回应的准确性和流畅性。

利用逻辑推理和相关性评分进行检索增强型神经生成回应

Retrieval-Augmented Neural Response Generation Using Logical Reasoning  and Relevance Scoring

Most prior work on task-oriented dialogue systems are restricted to a limited
coverage of domain APIs, while users oftentimes have domain related requests
that are not covered by the APIs. This challenge track aims to expand the
coverage of task-oriented dialogue systems by incorporating external
unstructured knowledge sources. We define three tasks: knowledge-seeking turn
detection, knowledge selection, and knowledge-grounded response generation. We
introduce the data sets and the neural baseline models for three tasks. The
challenge track received a total of 105 entries from 24 participating teams. In
the evaluation results, the ensemble methods with different large-scale
pretrained language models achieved high performances with improved knowledge
selection capability and better generalization into unseen data.

任务驱动的对话系统使用外部非结构化知识源来扩展任务领域 API 的覆盖范围，三个任务包括知识寻找、选择和响应生成，并用集成方法和大规模预训练语言模型取得了高性能。

DSTC9 中基于任务的对话建模与非结构化知识访问跟踪的超越领域 API

Beyond Domain APIs: Task-oriented Conversational Modeling with  Unstructured Knowledge Access Track in DSTC9

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。