Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021 · 42 citations · 23 references
ComputingComputer ScienceComputational LinguisticsNatural Language ProcessingReading ComprehensionConversational User InterfacesDialogue AnalysisDialogue SystemsLanguage ModelingNatural Language UnderstandingText ComprehensionDataset CurationDataset DescriptionsDialog SystemsDialogue UnderstandingEnd-to-end Dialogue SystemsGrounded Dialogue
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 goaloriented 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 documentbased context in the dataset. We present strong baseline approaches and various experimental results, aiming to support further research efforts on such a task. * Equal contribution.
23
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev et al. · 2016 · 6.3K citations · Full text
Reading Wikipedia to Answer Open-Domain Questions
Danqi Chen, Adam Fisch, Jason Weston et al. · 2017 · 1.4K citations · Full text