---
title: "Answer-focused and Position-aware Neural Question Generation"
description: "The paper tackles neural question generation: given a passage and a target answer span, the system… 15 references, cited by 71 papers here."
authors: ["Xingwu Sun", "Jing Liu", "Yajuan Lyu", "Wei He", "Yanjun Ma", "Shi Wang"]
year: 2018
venue: "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D18-1427/"
doi: "10.18653/v1/d18-1427"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/sun-2018-answer-focused-position-aware-neural/"
---
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# Answer-focused and Position-aware Neural Question Generation

**Authors:** Xingwu Sun, [Jing Liu](https://md.research.elamur.ai/free/en/authors/jing-liu/), [Yajuan Lyu](https://md.research.elamur.ai/free/en/authors/yajuan-lyu/), [Wei He](https://md.research.elamur.ai/free/en/authors/wei-he/), Yanjun Ma and Shi Wang · **Year:** 2018 · **Venue:** [Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2018](https://md.research.elamur.ai/free/en/venues/emnlp/2018/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/D18-1427/) · [PDF](https://aclanthology.org/D18-1427.pdf) · [DOI 10.18653/v1/d18-1427](https://doi.org/10.18653/v1/d18-1427) · [OpenAlex](https://openalex.org/W2889670144)

**Citations:** 184 citations (OpenAlex, 2026-10-14) · [71](https://md.research.elamur.ai/free/en/papers/sun-2018-answer-focused-position-aware-neural/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper tackles neural question generation: given a passage and a target answer span, the system must produce a fitting question. The authors reimplemented the strongest existing approach, a sequence-to-sequence model with copying and rich input features. Manual inspection of its output revealed two recurring errors. The question word often clashed with the answer type, for example "why" where "when" was needed. The model also tended to copy passage words located far from the answer instead of nearby relevant ones.

To fix these errors, they extend a feature-enriched pointer-generator in two ways:
- **Answer-focused component:** feeds an answer embedding directly into the choice of the interrogative word.
- **Position-aware component:** represents each context word's relative distance to the answer as a position embedding, which biases the copy attention toward nearby tokens.

A hybrid model combines both components. Experiments on SQuAD and MARCO show that the combination clearly beats the baseline and the previous state of the art. This matters for applications such as building reading-comprehension exercises, dialogue agents that ask questions, and creating QA datasets.

## Key points
- **Error analysis:** in 130 sampled baseline questions, 54 were judged poor. Of these, 20 (37.04%) had a mismatched question word and 11 (20.37%) copied distant, irrelevant context.
- **Baseline:** a pointer-generator (See et al., 2017) with a bidirectional LSTM encoder and the features from Zhou et al. (2017): NE, POS, and BIO answer-position tags.
- **Answer-focused fixes:** the answer-focused model repaired 7 of the 20 question-word errors, mostly cases where the answer itself reveals its type.
- **OOV copying:** the position-aware model raised the average precision of copied OOV words from 21.22% to 22.79%. It raised average recall from 18.87% to 20.62%.
- **Evaluation data:** SQuAD and MARCO.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Baidu](https://md.research.elamur.ai/free/en/companies/baidu/) — Company, Beijing, China · 40 works · 14,627 citations
- [Chinese Academy of Sciences](https://md.research.elamur.ai/free/en/government/chinese-academy-of-sciences/) — Government body, Beijing, China · 68 works · 22,753 citations
- [Institute of Computing Technology](https://md.research.elamur.ai/free/en/labs/institute-of-computing-technology/) — Research institute, Beijing, China · 18 works · 3,078 citations

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🧭 Topics (1): [❓ Question answering](https://md.research.elamur.ai/free/en/topics/question-answering/) · 243 works

## Cite

Cite as: Xingwu Sun, Jing Liu, Yajuan Lyu, Wei He, Yanjun Ma and Shi Wang. 2018. Answer-focused and Position-aware Neural Question Generation. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{sun2018answer,
  title = {{Answer-focused and Position-aware Neural Question Generation}},
  author = {Xingwu Sun, Jing Liu, Yajuan Lyu, Wei He, Yanjun Ma and Shi Wang},
  booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
  year = {2018},
  doi = {10.18653/v1/d18-1427},
  url = {https://doi.org/10.18653/v1/d18-1427}
}
```

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## References

15 references, 6 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 24 October 2026*