# Multi-hop Question Generation with Graph Convolutional Network

arXiv: https://arxiv.org/abs/2010.09240 · HTML: https://mlsanity.com/paper/2010.09240/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 20 of 20)

- [Read before Generate! Faithful Long Form Question Answering with Machine Reading](https://mlsanity.com/paper/2203.00343/) (2022)
- [RHO (ρ): Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding](https://mlsanity.com/paper/2212.01588/) (2022)
- [Controllable Open-ended Question Generation with A New Question Type Ontology](https://mlsanity.com/paper/2107.00152/) (2021)
- [MixQG: Neural Question Generation with Mixed Answer Types](https://mlsanity.com/paper/2110.08175/) (2021)
- [Knowledge-intensive Language Understanding for Explainable AI](https://mlsanity.com/paper/2108.01174/) (2021)
- [Towards Process-Oriented, Modular, and Versatile Question Generation that Meets Educational Needs](https://mlsanity.com/paper/2205.00355/) (2022)
- [GraphSearchNet: Enhancing GNNs via Capturing Global Dependencies for Semantic Code Search](https://mlsanity.com/paper/2111.02671/) (2021)
- [EduQG: A Multi-format Multiple Choice Dataset for the Educational Domain](https://mlsanity.com/paper/2210.06104/) (2022)
- [Few-Shot Data Synthesis for Open Domain Multi-Hop Question Answering](https://mlsanity.com/paper/2305.13691/) (2023)
- [Explainable Multi-hop Question Generation: An End-to-End Approach without Intermediate Question Labeling](https://mlsanity.com/paper/2404.00571/) (2024)
- [Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentation](https://mlsanity.com/paper/2211.01467/) (2022)
- [Downstream Transformer Generation of Question-Answer Pairs with Preprocessing and Postprocessing Pipelines](https://mlsanity.com/paper/2205.07387/) (2022)
- [Automatic Answerability Evaluation for Question Generation](https://mlsanity.com/paper/2309.12546/) (2023)
- [A Survey on Neural Question Generation: Methods, Applications, and Prospects](https://mlsanity.com/paper/2402.18267/) (2024)
- [Synthetic Context Generation for Question Generation](https://mlsanity.com/paper/2406.13188/) (2024)
- [QA4QG: Using Question Answering to Constrain Multi-Hop Question Generation](https://mlsanity.com/paper/2202.06538/) (2022)
- [Multi-Hop Question Generation via Dual-Perspective Keyword Guidance](https://mlsanity.com/paper/2505.15299/) (2025)
- [Automatic Generation of Multiple-Choice Questions](https://mlsanity.com/paper/2303.14576/) (2023)
- [ECIS-VQG: Generation of Entity-centric Information-seeking Questions from Videos](https://mlsanity.com/paper/2410.09776/) (2024)
- [Exploiting Intrinsic Duality for Multi-Hop Question Generation](https://mlsanity.com/paper/2608.00712/) (2026)

## Abstract (arXiv)

Multi-hop Question Generation (QG) aims to generate answer-related questions by aggregating and reasoning over multiple scattered evidence from different paragraphs. It is a more challenging yet under-explored task compared to conventional single-hop QG, where the questions are generated from the sentence containing the answer or nearby sentences in the same paragraph without complex reasoning. To address the additional challenges in multi-hop QG, we propose Multi-Hop Encoding Fusion Network for Question Generation (MulQG), which does context encoding in multiple hops with Graph Convolutional Network and encoding fusion via an Encoder Reasoning Gate. To the best of our knowledge, we are the first to tackle the challenge of multi-hop reasoning over paragraphs without any sentence-level information. Empirical results on HotpotQA dataset demonstrate the effectiveness of our method, in comparison with baselines on automatic evaluation metrics. Moreover, from the human evaluation, our proposed model is able to generate fluent questions with high completeness and outperforms the strongest baseline by 20.8% in the multi-hop evaluation. The code is publicly available at https://github.com/HLTCHKUST/MulQG}{https://github.com/HLTCHKUST/MulQG .