Response ranking in dialogues plays a crucial role in retrieval-based
conversational systems. In a multi-turn dialogue, to capture the gist of a
conversation, contextual information serves as essential knowledge to achieve
this goal. In this paper, we present a flexible neural framework that can
integrate contextual information from multiple channels. Specifically for the
current task, our approach is to provide two information channels in parallel,
Fusing Conversation history and domain knowledge extracted from Candidate
provenance (FCC), where candidate responses are curated, as contextual
information to improve the performance of multi-turn dialogue response ranking.
The proposed approach can be generalized as a module to incorporate
miscellaneous contextual features for other context-oriented tasks. We evaluate
our model on the MSDialog dataset widely used for evaluating conversational
response ranking tasks. Our experimental results show that our framework
significantly outperforms the previous state-of-the-art models, improving
Recall@1 by 7% and MAP by 4%. Furthermore, we conduct ablation studies to
evaluate the contributions of each information channel, and of the framework
components, to the overall ranking performance, providing additional insights
and directions for further improvements.

本论文提出了一种灵活的神经网络框架，利用上下文信息从多个渠道进行整合来提高多轮对话响应排序的性能。在 MSDialog 数据集上的实验结果表明，我们的框架显著优于先前的最先进模型，提高 Recall@1 7％和 MAP 4％。

融合会话历史和候选来源进行上下文响应排序的对话系统中的 FCC

FCC: Fusing Conversation History and Candidate Provenance for Contextual  Response Ranking in Dialogue Systems

We present a new approach to dialogue management using conversational
knowledge graphs as core representation of the dialogue state. To this end, we
introduce a new dataset, GraphWOZ, which comprises Wizard-of-Oz dialogues in
which human participants interact with a robot acting as a receptionist. In
contrast to most existing work on dialogue management, GraphWOZ relies on a
dialogue state explicitly represented as a dynamic knowledge graph instead of a
fixed set of slots. This graph is composed of a varying number of entities
(such as individuals, places, events, utterances and mentions) and relations
between them (such as persons being part of a group or attending an event). The
graph is then regularly updated on the basis of new observations and system
actions. GraphWOZ is released along with detailed manual annotations related to
the user intents, system responses, and reference relations occurring in both
user and system turns. Based on GraphWOZ, we present experimental results for
two dialogue management tasks, namely conversational entity linking and
response ranking. For conversational entity linking, we show how to connect
utterance mentions to their corresponding entity in the knowledge graph with a
neural model relying on a combination of both string and graph-based features.
Response ranking is then performed by summarizing the relevant content of the
graph into a text, which is concatenated with the dialogue history and employed
as input to score possible responses to a given dialogue state.

我们提出了一种使用对话知识图谱作为对话状态核心表示的新方法，提出了一个新数据集 GraphWOZ，并基于此展示了会话实体链接和响应排名的实验结果。

GraphWOZ: 基于对话知识图谱的对话管理

GraphWOZ: Dialogue Management with Conversational Knowledge Graphs

Intelligent personal assistant systems with either text-based or voice-based
conversational interfaces are becoming increasingly popular around the world.
Retrieval-based conversation models have the advantages of returning fluent and
informative responses. Most existing studies in this area are on open domain
"chit-chat" conversations or task / transaction oriented conversations. More
research is needed for information-seeking conversations. There is also a lack
of modeling external knowledge beyond the dialog utterances among current
conversational models. In this paper, we propose a learning framework on the
top of deep neural matching networks that leverages external knowledge for
response ranking in information-seeking conversation systems. We incorporate
external knowledge into deep neural models with pseudo-relevance feedback and
QA correspondence knowledge distillation. Extensive experiments with three
information-seeking conversation data sets including both open benchmarks and
commercial data show that, our methods outperform various baseline methods
including several deep text matching models and the state-of-the-art method on
response selection in multi-turn conversations. We also perform analysis over
different response types, model variations and ranking examples. Our models and
research findings provide new insights on how to utilize external knowledge
with deep neural models for response selection and have implications for the
design of the next generation of information-seeking conversation systems.

该研究提出了一种在深度神经匹配网络上利用外部知识进行响应排序的学习框架，实验证明，该方法在包括商业数据在内的三个信息寻求对话数据集上优于各种基线模型，包括多个深度文本匹配模型和多轮对话响应选择的最新方法。