Paper Info
Title
MultiDoc2Dial - Modeling Dialogues Grounded in Multiple Documents.
Abstract
We propose MultiDoc2Dial, a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents. Most previous works treat document-grounded dialogue modeling as a machine reading comprehension task based on a single given document or passage. In this work, we aim to address more realistic scenarios where a goal-oriented information-seeking conversation involves multiple topics, and hence is grounded on different documents. To facilitate such a task, we introduce a new dataset that contains dialogues grounded in multiple documents from four different domains. We also explore modeling the dialogue-based and document-based context in the dataset. We present strong baseline approaches and various experimental results, aiming to support further research efforts on such a task.
Year
DOI
Venue
2021
10.18653/v1/2021.emnlp-main.498
EMNLP
DocType
Volume
ISSN
Conference
2021.emnlp-main
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Song Feng128019.55
Siva Sankalp Patel200.34
Hui Wan311.39
Sachindra Joshi422125.75