Task-oriented dialogue systems aim at providing users with task-specific
services. Users of such systems often do not know all the information about the
task they are trying to accomplish, requiring them to seek information about
the task. To provide accurate and personalized task-oriented information
seeking results, task-oriented dialogue systems need to address two potential
issues: 1) users' inability to describe their complex information needs in
their requests; and 2) ambiguous/missing information the system has about the
users. In this paper, we propose a new Multi-Attention Seq2Seq Network, named
MAS2S, which can ask questions to clarify the user's information needs and the
user's profile in task-oriented information seeking. We also extend an existing
dataset for task-oriented information seeking, leading to the \ourdataset which
contains about 100k task-oriented information seeking dialogues that are made
publicly available\footnote{Dataset and code is available at
\href{https://github.com/sweetalyssum/clarit}{this https URL}.}.
Experimental results on \ourdataset show that MAS2S outperforms baselines on
both clarification question generation and answer prediction.

本文提出了一种名为 MAS2S 的多注意 Seq2Seq 网络，用于针对任务导向的信息检索中用户的信息需求和个人信息进行提问，扩展了现有数据集并在其上进行了实验，结果表明 MAS2S 在澄清问题生成和回答预测方面优于基线。

面向任务导向对话的信息寻求澄清问题研究

Towards Asking Clarification Questions for Information Seeking on  Task-Oriented Dialogues

Product descriptions on e-commerce websites often suffer from missing
important aspects. Clarification question generation (CQGen) can be a promising
approach to help alleviate the problem. Unlike traditional QGen assuming the
existence of answers in the context and generating questions accordingly, CQGen
mimics user behaviors of asking for unstated information. The generated CQs can
serve as a sanity check or proofreading to help e-commerce merchant to identify
potential missing information before advertising their product, and improve
consumer experience consequently. Due to the variety of possible user
backgrounds and use cases, the information need can be quite diverse but also
specific to a detailed topic, while previous works assume generating one CQ per
context and the results tend to be generic. We thus propose the task of Diverse
CQGen and also tackle the challenge of specificity. We propose a new model
named KPCNet, which generates CQs with Keyword Prediction and Conditioning, to
deal with the tasks. Automatic and human evaluation on 2 datasets (Home &
Kitchen, Office) showed that KPCNet can generate more specific questions and
promote better group-level diversity than several competing baselines.

研究了利用 CQGen 提高电子商务网站产品描述的效果，提出了 Diverse CQGen 和 KPCNet 模型，可以更准确地生成问题，提高消费者体验。

关键词驱动的多样化和具体化澄清问题生成

Diverse and Specific Clarification Question Generation with Keywords

Question answering and conversational systems are often baffled and need help
clarifying certain ambiguities. However, limitations of existing datasets
hinder the development of large-scale models capable of generating and
utilising clarification questions. In order to overcome these limitations, we
devise a novel bootstrapping framework (based on self-supervision) that assists
in the creation of a diverse, large-scale dataset of clarification questions
based on post-comment tuples extracted from stackexchange. The framework
utilises a neural network based architecture for classifying clarification
questions. It is a two-step method where the first aims to increase the
precision of the classifier and second aims to increase its recall. We
quantitatively demonstrate the utility of the newly created dataset by applying
it to the downstream task of question-answering. The final dataset, ClarQ,
consists of ~2M examples distributed across 173 domains of stackexchange. We
release this dataset in order to foster research into the field of
clarification question generation with the larger goal of enhancing dialog and
question answering systems.

通过基于自监督的新型启动框架，从 stackexchange 中提取 post-comment 元组，利用神经网络分类器构建了一个多样化、大规模的澄清问题数据集。将该数据集应用于问题回答的下游任务的实验表明其效用，并放出名为 ClarQ 的含 173 个领域的 2M 个实例的数据集。其目的是促进澄清问题生成的研究，以提升对话和问题回答系统。