CQuAE: A new Contextualized QUestion Answering corpus on Education domain

Abstract

Generating education-related questions and answers remains an open issue while being useful for students, teachers, and teaching aids. Given textual course material, we are interested in generating non-factual questions that require an elaborate answer (relying on analysis or reasoning). Despite the availability of annotated corpora of questions and answers, the effort to develop a generator using deep learning faces two main challenges. Firstly, freely accessible and qualitative data are insufficient to train generative approaches. Secondly, for a stand-alone application, we do not have explicit support to guide the generation toward complex questions. To tackle the first issue, we propose a new corpus based on education documents. For the second point, we propose to study several retargetable language algorithms to produce answers by extracting text spans from contextual documents to help the generation of questions. We particularly study the contribution of deep neural syntactic parsing and transformer-based semantic representation, taking into account the question type (according to our specific question typology) and the contextual support text span. Additionally, recent advances in generation models have proven the efficiency of the instruction-based approach for natural language generation. Consequently, we propose a first investigation of very large language models to generate questions related to the education domain.