This paper introduces QAConv, a new question answering (QA) dataset that uses
conversations as a knowledge source. We focus on informative conversations,
including business emails, panel discussions, and work channels. Unlike
open-domain and task-oriented dialogues, these conversations are usually long,
complex, asynchronous, and involve strong domain knowledge. In total, we
collect 34,608 QA pairs from 10,259 selected conversations with both
human-written and machine-generated questions. We use a question generator and
a dialogue summarizer as auxiliary tools to collect and recommend questions.
The dataset has two testing scenarios: chunk mode and full mode, depending on
whether the grounded partial conversation is provided or retrieved.
Experimental results show that state-of-the-art pretrained QA systems have
limited zero-shot performance and tend to predict our questions as
unanswerable. Our dataset provides a new training and evaluation testbed to
facilitate QA on conversations research.

该研究介绍了 QAConv，这是一个新的基于对话的问题回答（QA）数据集，通过长、复杂、异步和涉及强领域知识的信息型对话提供了一个新的训练和评估测试平台来促进 QA 在对话研究中的应用。