Generation of English Question Answer Exercises from Texts using Transformers based Models

This paper studies the use of NLP techniques, in particular, neural language models, for the generation of question/answer exercises from English texts. The experiments aim to generate beginner-level exercises from simple texts, to be used in teaching ESL (English as a Second Language) to children. The approach we present in this paper is based on four stages: a pre-processing stage that, among other basic tasks, applies a co-reference resolution tool; an answer candidate selection stage, which is based on semantic role labeling; a question generation stage, which takes as input the text with the resolved co-references and returns a set of questions for each answer candidate using a language model based on the Transformers architecture; and a post-processing stage that adjusts the format of the generated questions. The question generation model was evaluated on a benchmark obtaining similar results to those of previous works, and the complete pipeline was evaluated on a corpus specifically created for this task, achieving good results.