Transformer-Based Multi-Hop Question Generation (Student Abstract)


  • John Emerson University of Lethbridge
  • Yllias Chali University of Lethbridge



Natural Language Processing, NLP, Multi-hop Question Generation, Question Generation


Question generation is the parallel task of question answering, where given an input context and, optionally, an answer, the goal is to generate a relevant and fluent natural language question. Although recent works on question generation have experienced success by utilizing sequence-to-sequence models, there is a need for question generation models to handle increasingly complex input contexts to produce increasingly detailed questions. Multi-hop question generation is a more challenging task that aims to generate questions by connecting multiple facts from multiple input contexts. In this work, we apply a transformer model to the task of multi-hop question generation without utilizing any sentence-level supporting fact information. We utilize concepts that have proven effective in single-hop question generation, including a copy mechanism and placeholder tokens. We evaluate our model’s performance on the HotpotQA dataset using automated evaluation metrics, including BLEU, ROUGE and METEOR and show an improvement over the previous work.




How to Cite

Emerson, J., & Chali, Y. (2023). Transformer-Based Multi-Hop Question Generation (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16206-16207.