A Neural Question Answering System for Supporting Software Engineering Students

QA (Question Answering) is the task of automatically answer natural language questions posed by humans. Usually, QA approaches use a combination of computational linguistics, information retrieval and knowledge representation to find answers for questions. In a teaching-learning process, it is critical that teachers use a range of teaching strategies to effectively meet the needs of individual learners. Thus, QA approaches can be effectively used to support the teaching learning process. In this article, we exploit neural networks for QA to support the teaching-learning process. Particularly, we use DMN+ (improved dynamic memory networks) and SeqToSeq (sequence to sequence) with a corpus of SE (software engineering) texts to effectively answer questions commonly posed by SE learners. Experimental results show that DMN+ is more effective than SeqToSeq for this task with up to 77% accuracy.