We introduce doc2dial, a new dataset of goal-oriented dialogues that are grounded in the associated documents. Inspired by how the authors compose documents for guiding end users, we first construct dialogue flows based on the content elements that corresponds to higher-level relations across text sections as well as lower-level relations between discourse units within a section. Then we present these dialogue flows to crowd contributors to create conversational utterances. The dataset includes about 4800 annotated conversations with an average of 14 turns that are grounded in over 480 documents from four domains. Compared to the prior document-grounded dialogue datasets, this dataset covers a variety of dialogue scenes in information-seeking conversations. For evaluating the versatility of the dataset, we introduce multiple dialogue modeling tasks and present baseline approaches.
@article{arxiv.2011.06623,
title = {doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset},
author = {Song Feng and Hui Wan and Chulaka Gunasekara and Siva Sankalp Patel and Sachindra Joshi and Luis A. Lastras},
journal= {arXiv preprint arXiv:2011.06623},
year = {2020}
}