Initial Experiments on Question Answering from the Intrinsic Structure of Oral History Archives.

Large audio archives with spoken content are natural candidates for question answering systems. Oral history archives generally contain many facts and stories that would be otherwise hard to obtain without listening to hours of recordings. We strive for making the archive more accessible by allowing natural language question answering. In this paper, we present challenges our dataset poses. We propose our initial approach that uses questions and answers mined from the archive itself and evaluate the performance in experiments with pretrained language representation and question answering models.