Multi-hop Question Generation without Supporting Fact Information
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 with the goal of producing increasingly elaborate 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 and human evaluation, and show an improvement over the previous works.
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Copyright (c) 2023 Yllias Chali, John Emerson
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.