# Improving Question Generation With to the Point Context

arXiv: https://arxiv.org/abs/1910.06036 · HTML: https://mlsanity.com/paper/1910.06036/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2019.

## Built on by (most cited first, 15 of 15)

- [Controllable Open-ended Question Generation with A New Question Type Ontology](https://mlsanity.com/paper/2107.00152/) (2021)
- [HICF: Hyperbolic Informative Collaborative Filtering](https://mlsanity.com/paper/2207.09051/) (2022)
- [Multiple-Choice Question Generation: Towards an Automated Assessment Framework](https://mlsanity.com/paper/2209.11830/) (2022)
- [ProQA: Structural Prompt-based Pre-training for Unified Question Answering](https://mlsanity.com/paper/2205.04040/) (2022)
- [Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask](https://mlsanity.com/paper/2204.02908/) (2022)
- [ChainCQG: Flow-Aware Conversational Question Generation](https://mlsanity.com/paper/2102.02864/) (2021)
- [Learning by Analogy: Diverse Questions Generation in Math Word Problem](https://mlsanity.com/paper/2306.09064/) (2023)
- [Text Revision by On-the-Fly Representation Optimization](https://mlsanity.com/paper/2204.07359/) (2022)
- [GTM: A Generative Triple-Wise Model for Conversational Question Generation](https://mlsanity.com/paper/2106.03635/) (2021)
- [Attentive Knowledge-aware Graph Convolutional Networks with Collaborative Guidance for Personalized Recommendation](https://mlsanity.com/paper/2109.02046/) (2021)
- [Open-Retrieval Conversational Machine Reading](https://mlsanity.com/paper/2102.08633/) (2021)
- [Graph Component Contrastive Learning for Concept Relatedness Estimation](https://mlsanity.com/paper/2206.12556/) (2022)
- [CONSISTENT: Open-Ended Question Generation From News Articles](https://mlsanity.com/paper/2210.11536/) (2022)
- [A Survey on Neural Question Generation: Methods, Applications, and Prospects](https://mlsanity.com/paper/2402.18267/) (2024)
- [Enhancing Question Generation with Commonsense Knowledge](https://mlsanity.com/paper/2106.10454/) (2021)

## Abstract (arXiv)

Question generation (QG) is the task of generating a question from a reference sentence and a specified answer within the sentence. A major challenge in QG is to identify answer-relevant context words to finish the declarative-to-interrogative sentence transformation. Existing sequence-to-sequence neural models achieve this goal by proximity-based answer position encoding under the intuition that neighboring words of answers are of high possibility to be answer-relevant. However, such intuition may not apply to all cases especially for sentences with complex answer-relevant relations. Consequently, the performance of these models drops sharply when the relative distance between the answer fragment and other non-stop sentence words that also appear in the ground truth question increases. To address this issue, we propose a method to jointly model the unstructured sentence and the structured answer-relevant relation (extracted from the sentence in advance) for question generation. Specifically, the structured answer-relevant relation acts as the to the point context and it thus naturally helps keep the generated question to the point, while the unstructured sentence provides the full information. Extensive experiments show that to the point context helps our question generation model achieve significant improvements on several automatic evaluation metrics. Furthermore, our model is capable of generating diverse questions for a sentence which conveys multiple relations of its answer fragment.