Enhancing Diversity in Difficulty-Controllable Question Generation for Reading Comprehension via Extended T5
DOI:
https://doi.org/10.58459/icce.2024.4813Abstract
Recently, automatic generation of reading comprehension questions with controllable difficulty levels has attracted growing interest for educational purposes. The latest method for difficulty-controllable question generation employs a two-stage mechanism utilizing two independent large language models. Specifically, given a reading passage and a difficulty level as inputs, it first produces a reference answer using BERT and then generates a corresponding question using GPT-2. However, this two-stage approach has the limitation that the questions generated depend strongly on the reference answers produced beforehand, restricting the diversity of questions. To overcome this limitation, we propose an end-to-end method that enables the simultaneous generation of questions and reference answers by extending T5, a large language model with an encoder mechanism equivalent to BERT and a decoder mechanism equivalent to GPT-2. In our method, T5 is extended to generate answers from its encoder and questions from its decoder, with the encoder's output vector passed to the decoder. Experiments using a benchmark dataset demonstrate that our method significantly improves the diversity of both questions and answers compared with the conventional method while maintaining difficulty controllability.