Request Correction

Question Generation and Answering for exploring Digital Humanities collections

This paper introduces the question answering paradigm as a way to explore digitized archive collections for Social Science studies. In particular, we are interested in evaluating largely studied question generation and question answering approaches on a new type of documents, as a step forward beyond traditional benchmark evaluations. Question generation can be used as a way to provide enhanced training material for Machine Reading Question Answering algorithms but also has its own purpose in this paradigm, where relevant questions can be used as a way to create explainable links between documents. To this end, generating large amounts of question is not the only motivation, but we need to include qualitative and semantic control to the generation process. We propose a new approach for question generation, relying on a BART Transformer based generative model, for which input data are enriched by semantic constraints. Question generation and answering are evaluated on several French corpora, and the whole approach is validated on a new corpus of digitized archive collection of a French Social Science journal.