Open-domain dialog involves generating search queries that help obtain
relevant knowledge for holding informative conversations. However, it can be
challenging to determine what information to retrieve when the user is passive
and does not express a clear need or request. To tackle this issue, we present
a novel approach that focuses on generating internet search queries that are
guided by social commonsense. Specifically, we leverage a commonsense dialog
system to establish connections related to the conversation topic, which
subsequently guides our query generation. Our proposed framework addresses
passive user interactions by integrating topic tracking, commonsense response
generation and instruction-driven query generation. Through extensive
evaluations, we show that our approach overcomes limitations of existing query
generation techniques that rely solely on explicit dialog information, and
produces search queries that are more relevant, specific, and compelling,
ultimately resulting in more engaging responses.

通过整合主题跟踪、常识回答生成和指令驱动的查询生成，我们的研究提出了一种新的方法来生成由社交常识引导的互联网搜索查询，在 passsive 用户交互中能够生成相关、具体和有吸引力的搜索查询，从而提供更加引人入胜的回应。

面向开放领域知识驱动对话的社交常识引导搜索查询生成

Social Commonsense-Guided Search Query Generation for Open-Domain  Knowledge-Powered Conversations

Many efforts have been made to construct dialog systems for different types
of conversations, such as task-oriented dialog (TOD) and open-domain dialog
(ODD). To better mimic human-level conversations that usually fuse various
dialog modes, it is essential to build a system that can effectively handle
both TOD and ODD and access different knowledge sources. To address the lack of
available data for the fused task, we propose a framework for automatically
generating dialogues that combine knowledge-grounded ODDs and TODs in various
settings. Additionally, we introduce a unified model PivotBot that is capable
of appropriately adopting TOD and ODD modes and accessing different knowledge
sources in order to effectively tackle the fused task. Evaluation results
demonstrate the superior ability of the proposed model to switch seamlessly
between TOD and ODD tasks.

提出了一种自动生成结合任务导向对话和开放领域对话的框架，同时介绍了一种适用于这一任务的统一模型 PivotBot，并通过评估结果证明，该模型能够在任务导向对话和开放领域对话之间无缝切换。

利用合成的开放领域对话提高任务机器人的参与度

Enhancing Task Bot Engagement with Synthesized Open-Domain Dialog

Large end-to-end neural open-domain chatbots are becoming increasingly
popular. However, research on building such chatbots has typically assumed that
the user input is written in nature and it is not clear whether these chatbots
would seamlessly integrate with automatic speech recognition (ASR) models to
serve the speech modality. We aim to bring attention to this important question
by empirically studying the effects of various types of synthetic and actual
ASR hypotheses in the dialog history on TransferTransfo, a state-of-the-art
Generative Pre-trained Transformer (GPT) based neural open-domain dialog system
from the NeurIPS ConvAI2 challenge. We observe that TransferTransfo trained on
written data is very sensitive to such hypotheses introduced to the dialog
history during inference time. As a baseline mitigation strategy, we introduce
synthetic ASR hypotheses to the dialog history during training and observe
marginal improvements, demonstrating the need for further research into
techniques to make end-to-end open-domain chatbots fully speech-robust. To the
best of our knowledge, this is the first study to evaluate the effects of
synthetic and actual ASR hypotheses on a state-of-the-art neural open-domain
dialog system and we hope it promotes speech-robustness as an evaluation
criterion in open-domain dialog.

本文研究了 Generative Pre-trained Transformer 模型训练过程中不同类型的合成和真实的 ASR 假设对于模型的影响，并提出一种基线缓解策略。结果表明，模型在创作过程中，对话历史中引入 ASR 假设很敏感。因此，需要进一步探索增强神经聊天机器人语音健壮性的技术。