# Improving Neural Question Generation using Answer Separation

arXiv: https://arxiv.org/abs/1809.02393 · HTML: https://mlsanity.com/paper/1809.02393/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2018.

## Built on by (most cited first, 30 of 30)

- [Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting](https://mlsanity.com/paper/2105.11698/) (2021)
- [ArchivalQA: A Large-scale Benchmark Dataset for Open Domain Question Answering over Historical News Collections](https://mlsanity.com/paper/2109.03438/) (2021)
- [Multiple-Choice Question Generation: Towards an Automated Assessment Framework](https://mlsanity.com/paper/2209.11830/) (2022)
- [PaniniQA: Enhancing Patient Education Through Interactive Question Answering](https://mlsanity.com/paper/2308.03253/) (2023)
- [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)
- [GTM: A Generative Triple-Wise Model for Conversational Question Generation](https://mlsanity.com/paper/2106.03635/) (2021)
- [A Survey of Natural Language Generation](https://mlsanity.com/paper/2112.11739/) (2021)
- [QAScore -- An Unsupervised Unreferenced Metric for the Question Generation Evaluation](https://mlsanity.com/paper/2210.04320/) (2022)
- [Improving Question Generation with Multi-level Content Planning](https://mlsanity.com/paper/2310.13512/) (2023)
- [Does Synthetic Data Make Large Language Models More Efficient?](https://mlsanity.com/paper/2310.07830/) (2023)
- [OneStop QAMaker: Extract Question-Answer Pairs from Text in a One-Stop Approach](https://mlsanity.com/paper/2102.12128/) (2021)
- [I Do Not Understand What I Cannot Define: Automatic Question Generation With Pedagogically-Driven Content Selection](https://mlsanity.com/paper/2110.04123/) (2021)
- [Closed-book Question Generation via Contrastive Learning](https://mlsanity.com/paper/2210.06781/) (2022)
- [Downstream Transformer Generation of Question-Answer Pairs with Preprocessing and Postprocessing Pipelines](https://mlsanity.com/paper/2205.07387/) (2022)
- [Proactive and Reactive Engagement of Artificial Intelligence Methods for Education: A Review](https://mlsanity.com/paper/2301.10231/) (2023)
- [A Survey on Neural Question Generation: Methods, Applications, and Prospects](https://mlsanity.com/paper/2402.18267/) (2024)
- [EHRTutor: Enhancing Patient Understanding of Discharge Instructions](https://mlsanity.com/paper/2310.19212/) (2023)
- [Quinductor: a multilingual data-driven method for generating reading-comprehension questions using Universal Dependencies](https://mlsanity.com/paper/2103.10121/) (2021)
- [Automated question generation and question answering from Turkish texts](https://mlsanity.com/paper/2111.06476/) (2021)
- [QA4QG: Using Question Answering to Constrain Multi-Hop Question Generation](https://mlsanity.com/paper/2202.06538/) (2022)
- [Enhancing Question Generation with Commonsense Knowledge](https://mlsanity.com/paper/2106.10454/) (2021)
- [Automatic Generation of Multiple-Choice Questions](https://mlsanity.com/paper/2303.14576/) (2023)
- [Bringing legal knowledge to the public by constructing a legal question bank using large-scale pre-trained language model](https://mlsanity.com/paper/2505.04132/) (2025)
- [Enhancing Pre-trained Models with Text Structure Knowledge for Question Generation](https://mlsanity.com/paper/2209.04179/) (2022)
- [Tag-Set-Sequence Learning for Generating Question-Answer Pairs](https://mlsanity.com/paper/2210.11608/) (2022)
- [How to Build Robust FAQ Chatbot with Controllable Question Generator?](https://mlsanity.com/paper/2112.03007/) (2021)
- [Obj2Sub: Unsupervised Conversion of Objective to Subjective Questions](https://mlsanity.com/paper/2206.11848/) (2022)
- [Exploring Answer Information Methods for Question Generation with Transformers](https://mlsanity.com/paper/2312.03483/) (2023)
- [KT4EQG: Personalized Exercise Question Generation via Knowledge Tracing](https://mlsanity.com/paper/2605.23933/) (2026)

## Abstract (arXiv)

Neural question generation (NQG) is the task of generating a question from a given passage with deep neural networks. Previous NQG models suffer from a problem that a significant proportion of the generated questions include words in the question target, resulting in the generation of unintended questions. In this paper, we propose answer-separated seq2seq, which better utilizes the information from both the passage and the target answer. By replacing the target answer in the original passage with a special token, our model learns to identify which interrogative word should be used. We also propose a new module termed keyword-net, which helps the model better capture the key information in the target answer and generate an appropriate question. Experimental results demonstrate that our answer separation method significantly reduces the number of improper questions which include answers. Consequently, our model significantly outperforms previous state-of-the-art NQG models.