Identifying relevant Persona or Knowledge for conversational systems is a
critical component of grounded dialogue response generation. However, each
grounding has been studied in isolation with more practical multi-context tasks
only recently introduced. We define Persona and Knowledge Dual Context
Identification as the task to identify Persona and Knowledge jointly for a
given dialogue, which could be of elevated importance in complex multi-context
Dialogue settings. We develop a novel grounding retrieval method that utilizes
all contexts of dialogue simultaneously while also requiring limited training
via zero-shot inference due to compatibility with neural Q \& A retrieval
models. We further analyze the hard-negative behavior of combining Persona and
Dialogue via our novel null-positive rank test.

本文提出使用 neural Q＆A 模型结合全文对话上下文一起识别人设和知识的任务来识别相关 Persona 或 Knowledge，以应对多重上下文的对话系统，通过建立新的 grounding retrieval 方法来实现。

PK-ICR: 面向基于语境的对话的人物知识交互式检索

PK-ICR: Persona-Knowledge Interactive Context Retrieval for Grounded Dialogue

Dialogue participants often refer to entities or situations repeatedly within
a conversation, which contributes to its cohesiveness. Subsequent references
exploit the common ground accumulated by the interlocutors and hence have
several interesting properties, namely, they tend to be shorter and reuse
expressions that were effective in previous mentions. In this paper, we tackle
the generation of first and subsequent references in visually grounded
dialogue. We propose a generation model that produces referring utterances
grounded in both the visual and the conversational context. To assess the
referring effectiveness of its output, we also implement a reference resolution
system. Our experiments and analyses show that the model produces better, more
effective referring utterances than a model not grounded in the dialogue
context, and generates subsequent references that exhibit linguistic patterns
akin to humans.

本研究提出了一种基于视觉和对话环境的生成模型来产生有效指代话语，并实现了一个参照解析系统进行评估。实验结果表明，相比其它非对话背景下的模型，该模型能够产生更加有效的指代话语，而且在生成具有人类语言模式的后续指代方面表现出良好的效果。

在视觉和对话环境中生成后续参照：引用、重用、减少

Refer, Reuse, Reduce: Generating Subsequent References in Visual and  Conversational Contexts

We introduce a large scale crowdsourced text adventure game as a research
platform for studying grounded dialogue. In it, agents can perceive, emote, and
act whilst conducting dialogue with other agents. Models and humans can both
act as characters within the game. We describe the results of training
state-of-the-art generative and retrieval models in this setting. We show that
in addition to using past dialogue, these models are able to effectively use
the state of the underlying world to condition their predictions. In
particular, we show that grounding on the details of the local environment,
including location descriptions, and the objects (and their affordances) and
characters (and their previous actions) present within it allows better
predictions of agent behavior and dialogue. We analyze the ingredients
necessary for successful grounding in this setting, and how each of these
factors relate to agents that can talk and act successfully.

本文介绍了一个大规模的众包文字冒险游戏作为研究平台，用于研究基于场景的对话。在其中，代理可以感知，表情和行动，同时与其他代理进行对话。我们介绍了在这种情况下训练最先进的生成模型和检索模型的结果。我们展示了除了过去的对话，这些模型还能够有效地使用潜在世界的状态来调整它们的预测，特别是使用本地环境的详细信息，包括位置描述和其中存在的对象（及其可用性）和角色（及其先前的行动），可以更好地预测代理行为和对话。我们分析了在这种设置中成功落地所需的要素，以及每个因素如何与能够成功说话和行动的代理相关。