---
title: "Learning to Ask — Du 2017"
description: "The paper tackles automatic question generation (QG) for reading comprehension: given a sentence from a passage, the system produces a natural question…"
authors: ["Xinya Du", "Junru Shao", "Claire Cardie"]
year: 2017
venue: "arXiv:1705.00106"
source: "https://arxiv.org/abs/1705.00106"
doi: "10.48550/arxiv.1705.00106"
arxiv: "1705.00106"
licence: "arXiv — non-exclusive licence to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/du-2017-learning-ask-neural-question-generation/"
---
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# Learning to Ask: Neural Question Generation for Reading Comprehension

**Authors:** [Xinya Du](https://md.research.elamur.ai/free/en/authors/xinya-du/), Junru Shao, [Claire Cardie](https://md.research.elamur.ai/free/en/authors/claire-cardie/) · **Year:** 2017 · **Venue:** arXiv:1705.00106 · **Licence:** [arXiv — non-exclusive licence to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1705.00106) · [PDF](https://arxiv.org/pdf/1705.00106) · [DOI 10.48550/arxiv.1705.00106](https://doi.org/10.48550/arxiv.1705.00106) · [arXiv 1705.00106](https://arxiv.org/abs/1705.00106) · [OpenAlex](https://openalex.org/W2962717047)

**Citations:** 688 citations (openalex-snapshot · 2026-10-14) · [225](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper tackles automatic question generation (QG) for reading comprehension: given a sentence from a passage, the system produces a natural question about its content. Earlier systems relied on handcrafted declarative-to-question transformation rules, often followed by a feature-based ranker. Their questions tend to copy the source wording and are therefore easy to answer. The authors instead treat QG as end-to-end sequence-to-sequence learning. They use an LSTM encoder-decoder with global attention, trained on sentence-question pairs from SQuAD and requiring no rules or NLP pipeline. A second variant also encodes the surrounding paragraph, and they test pre-trained versus learned word embeddings. On automatic metrics such as BLEU and METEOR, the neural model clearly beats an IR baseline, a phrase-based SMT system and the rule-based Heilman & Smith (H&S) system. Human judges also found its questions more grammatical, fluent and harder to answer. The work matters because it shows that data-driven abstractive generation can paraphrase and compress source text. That makes it useful for building educational materials and for creating QA datasets.

## Key points
- **Architecture:** a bidirectional LSTM encodes the sentence, and an LSTM decoder with Luong-style global attention (bilinear scoring) generates the question. The paragraph-aware variant uses a "Y"-shaped design: a second BiLSTM encodes the paragraph, and both encodings are concatenated to initialize the decoder.
- **Baselines:** the system is compared on SQuAD against IR, an SMT system (Moses), DirectIn (which outputs the input sentence directly) and H&S. DirectIn and H&S score similarly because both stay very close to the source wording.
- **Embeddings and paragraph context:** pre-trained word embeddings generally improve results. Adding paragraph context slightly lowers overall automatic scores, which the authors attribute to noise in the paragraph.
- **Human evaluation:** the system outperforms H&S on every criterion. It was ranked best in 38.4% of judgments, with an average rank of 1.94 and Krippendorff's Alpha of 0.236.
- **Dev-set analysis:** in a sample of 346 dev pairs, about 70% of questions need only the sentence and about 20% need wider paragraph context. Roughly 9.5% were judged not askable. The paragraph-level model performs best on the questions that need paragraph context.

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

Fields: [Other](https://md.research.elamur.ai/free/en/shelves/other/)

Topics: [Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/)

## Cited by

Cited by

225 papers of our library cite it (of the 140673 whose bibliographies we read); 12 of them have a page here, 213 not yet.

- [Synthetic QA Corpora Generation with Roundtrip Consistency](https://md.research.elamur.ai/free/en/papers/alberti-2019-synthetic-qa-corpora-generation-roundtrip/)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://md.research.elamur.ai/free/en/papers/dong-2019-unified-language-model-pre-training/)
- [Event Extraction by Answering (Almost) Natural Questions](https://md.research.elamur.ai/free/en/papers/du-2020-event-extraction-answering-almost-natural/)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://md.research.elamur.ai/free/en/papers/du-2022-glm-general-language-model-pretraining/)
- [Question Generation for Question Answering](https://md.research.elamur.ai/free/en/papers/duan-2017-question-generation-question-answering/)
- [Inquisitive Question Generation for High Level Text Comprehension](https://md.research.elamur.ai/free/en/papers/ko-2020-inquisitive-question-generation-high-level/)
- [PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them](https://md.research.elamur.ai/free/en/papers/lewis-2021-paq-65-million-probably-asked/)
- [SPARTQA: A Textual Question Answering Benchmark for Spatial Reasoning](https://md.research.elamur.ai/free/en/papers/mirzaee-2021-spartqa-textual-question-answering-benchmark/)
- [Training Question Answering Models From Synthetic Data](https://md.research.elamur.ai/free/en/papers/puri-2020-training-question-answering-models-synthetic/)
- [ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training](https://md.research.elamur.ai/free/en/papers/qi-2020-prophetnet-predicting-future-n-gram/)
- [Generative Data Augmentation for Commonsense Reasoning](https://md.research.elamur.ai/free/en/papers/yang-2020-generative-data-augmentation-commonsense-reasoning/)
- [It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story Books](https://md.research.elamur.ai/free/en/papers/yao-2022-it-is-ai-s-turn/)

## Cite

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

```bibtex
@misc{du2017learning,
  title = {{Learning to Ask: Neural Question Generation for Reading Comprehension}},
  author = {Du, Xinya and Shao, Junru and Cardie, Claire},
  year = {2017},
  doi = {10.48550/arxiv.1705.00106},
  eprint = {1705.00106},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1705.00106}
}
```

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## References

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