This paper introducesℚ𝔸ℂ𝕠𝕟𝕧,111Data and code are available athttps://github.com/salesforce/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.