---
title: "Unsupervised Question Answering by Cloze Translation (2019)"
description: "They build a synthetic data generator in three steps. 56.4 F1 on SQuAD. 48 references, cited by 67 papers here."
authors: ["Patrick Lewis", "Ludovic Denoyer", "Sebastian Riedel"]
year: 2019
venue: "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics"
source: "https://aclanthology.org/P19-1484/"
doi: "10.18653/v1/p19-1484"
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/lewis-2019-unsupervised-question-answering-cloze-translation/"
---
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# Unsupervised Question Answering by Cloze Translation

**Authors:** [Patrick Lewis](https://md.research.elamur.ai/free/en/authors/patrick-lewis/), [Ludovic Denoyer](https://md.research.elamur.ai/free/en/authors/ludovic-denoyer/) and [Sebastian Riedel](https://md.research.elamur.ai/free/en/authors/sebastian-riedel/) · **Year:** 2019 · **Venue:** [Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2019](https://md.research.elamur.ai/free/en/venues/acl/2019/)) · **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/P19-1484/) · [PDF](https://aclanthology.org/P19-1484.pdf) · [DOI 10.18653/v1/p19-1484](https://doi.org/10.18653/v1/p19-1484) · [OpenAlex](https://openalex.org/W2963430447)

**Citations:** 125 citations (OpenAlex, 2026-10-14) · [67](https://md.research.elamur.ai/free/en/papers/lewis-2019-unsupervised-question-answering-cloze-translation/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Extractive QA systems usually depend on large, manually annotated question–answer collections, which exist for only a few languages and domains. Lewis, Denoyer and Riedel ask whether such a system can be trained with no aligned (question, context, answer) data. They build a synthetic data generator in three steps. First, they sample paragraphs from English Wikipedia. Next, they pick noun phrases or named entity mentions as answers and mask them to form cloze statements. Finally, they turn these clozes into questions. For that last step, they compare four options: identity mapping, noise injection, the Heilman and Smith rule-based generator, and an unsupervised seq2seq translator. The translator is trained on unpaired corpora of clozes and real questions using denoising auto-encoding and online back-translation. A standard QA model trained only on this generated data reaches 56.4 F1 on SQuAD v1, which beats early supervised baselines. The approach also helps when only a few labelled examples exist. This makes it relevant for under-resourced settings, though its best variant still relies on an NER system and a parser.

## Key points
- Without any SQuAD training data, the model scores 56.4 F1 on SQuAD v1, and 64.5 F1 on questions whose answer is a named entity.
- In a few-shot setting with 32 labelled examples, the approach gives 59.3 F1, compared with 40.0 F1 without it.
- The cloze corpus contains 5M items drawn from Wikipedia. The best pipeline uses NER trained on OntoNotes 5 and a constituency parser trained on the Penn Treebank.
- The crude "noisy cloze" variant almost matches the best system, even though only 2.7% of its questions are well-formed. This suggests SQuAD can be solved largely through word overlap.
- The rule-based translator performs worse than both the noise-based and unsupervised seq2seq approaches. Data and models are released at github.com/facebookresearch/UnsupervisedQA.

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

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## Cite

Cite as: Patrick Lewis, Ludovic Denoyer and Sebastian Riedel. 2019. Unsupervised Question Answering by Cloze Translation. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.

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

```bibtex
@inproceedings{lewis2019unsupervised,
  title = {{Unsupervised Question Answering by Cloze Translation}},
  author = {Patrick Lewis, Ludovic Denoyer and Sebastian Riedel},
  booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
  year = {2019},
  doi = {10.18653/v1/p19-1484},
  url = {https://doi.org/10.18653/v1/p19-1484}
}
```

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*Updated 24 October 2026*