CAsT-19: A Dataset for Conversational Information Seeking
Jeff Dalton, Chenyan Xiong, Vaibhav Kumar, Jamie Callan
Abstract
CAsT-19 is a new dataset that supports research on conversational information seeking. The corpus is 38,426,252 passages from the TREC Complex Answer Retrieval (CAR) and Microsoft MAchine Reading COmprehension (MARCO) datasets. Eighty information seeking dialogues (30 train, 50 test) are an average of 9 to 10 questions long. A dialogue may explore a topic broadly or drill down into subtopics. Questions contain ellipsis, implied context, mild topic shifts, and other characteristics of human conversation that may prevent them from being understood in isolation. Relevance assessments are provided for 30 training topics and 20 test topics.
Topics & Concepts
Ellipsis (linguistics)ConversationComputer scienceRelevance (law)Context (archaeology)Test (biology)Natural language processingComprehensionInformation retrievalArtificial intelligenceIsolation (microbiology)Information seekingReading (process)World Wide WebLinguisticsProgramming languageLawPhilosophyBiologyPolitical scienceMicrobiologyPaleontologyTopic ModelingSpeech and dialogue systemsNatural Language Processing Techniques