  Understanding and characterizing how people interact in information-seeking
conversations is crucial in developing conversational search systems. In this
paper, we introduce a new dataset designed for this purpose and use it to
analyze information-seeking conversations by user intent distribution,
co-occurrence, and flow patterns. The MSDialog dataset is a labeled dialog
dataset of question answering (QA) interactions between information seekers and
providers from an online forum on Microsoft products. The dataset contains more
than 2,000 multi-turn QA dialogs with 10,000 utterances that are annotated with
user intent on the utterance level. Annotations were done using crowdsourcing.
With MSDialog, we find some highly recurring patterns in user intent during an
information-seeking process. They could be useful for designing conversational
search systems. We will make our dataset freely available to encourage
exploration of information-seeking conversation models.
