---
title: "Zero-shot Neural Passage Retrieval via Domain-targeted Synthetic Question Generation (2021)"
description: "The work therefore offers a practical way to build neural retrieval for domains that lack annotations. 63 references, cited by 64 papers here."
authors: ["Ji Ma", "Ivan Korotkov", "Yinfei Yang", "Keith Hall", "Ryan McDonald"]
year: 2021
venue: "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume"
source: "https://aclanthology.org/2021.eacl-main.92/"
doi: "10.18653/v1/2021.eacl-main.92"
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/ma-2021-zero-shot-neural-passage-retrieval/"
---
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# Zero-shot Neural Passage Retrieval via Domain-targeted Synthetic Question Generation

**Authors:** [Ji Ma](https://md.research.elamur.ai/free/en/authors/ji-ma/), Ivan Korotkov, [Yinfei Yang](https://md.research.elamur.ai/free/en/authors/yinfei-yang/), [Keith Hall](https://md.research.elamur.ai/free/en/authors/keith-hall/) and [Ryan McDonald](https://md.research.elamur.ai/free/en/authors/ryan-mcdonald/) · **Year:** 2021 · **Venue:** [Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume](https://md.research.elamur.ai/free/en/venues/eacl/) ([EACL 2021](https://md.research.elamur.ai/free/en/venues/eacl/2021/)) · **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/2021.eacl-main.92/) · [PDF](https://aclanthology.org/2021.eacl-main.92.pdf) · [DOI 10.18653/v1/2021.eacl-main.92](https://doi.org/10.18653/v1/2021.eacl-main.92) · [OpenAlex](https://openalex.org/W3156836409)

**Citations:** 80 citations (OpenAlex, 2026-10-14) · [64](https://md.research.elamur.ai/free/en/papers/ma-2021-zero-shot-neural-passage-retrieval/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Neural first-stage passage retrievers usually need large labelled training sets before they beat term-based methods like BM25. Such sets rarely exist in specialized settings like enterprise search or scientific search. Ma et al. address this zero-shot case, where only the target passage collection is available.

They train a Transformer encoder-decoder question generator on general-domain community QA data. They then run it over passages from the target domain, which yields an unlimited supply of noisy but domain-specific question-passage pairs for training a dense retriever. They also propose a hybrid first-stage model that combines BM25 with the neural scores. Both are expressed as vector similarity, so exact nearest-neighbour search covers the whole collection.

The method was evaluated on scientific (BioASQ), travel and Ubuntu forum collections and on Natural Questions. Synthetic training clearly beats unsupervised and out-of-domain neural baselines, and the hybrid is the strongest zero-shot system overall. On BioASQ the hybrid performs about as well as supervised challenge entries. The work therefore offers a practical way to build neural retrieval for domains that lack annotations.

## Key points
- **Generator training data:** about 2 million English question-answer pairs from StackExchange and Yahoo! Answers.
  - Only pairs with at least one positive user rating were kept.
  - Average length is roughly 12 tokens per question and 155 per answer.
- **Baselines:** BM25, ICT, Ngram and an out-of-domain QA-trained model. QGen beats every other neural model, and QGenHyb is best in each domain.
  - On BioASQ7, QGenHyb reaches roughly 52 nDCG@10.
  - On Natural Questions, MRR is 16.73 for QGenHyb, 14.93 for QGen and 6.63 for BM25, versus 33.68 for a supervised model.
- **BioASQ8 document retrieval:** QGenHyb averages about 39.3 MAP, in line with supervised entries such as AUEB-1. Adding a rescorer lifts this to about 40.4.
- **Coverage experiment:** generating questions for only 20% of the documents already gives peak performance. This suggests the model generalizes rather than memorizing the corpus.
- **Generator size:** larger generators (Lite with 3 layers, Base with 12, Large with 24) improve Rouge scores but barely change retrieval quality. Having large amounts of domain-targeted data matters more than generator size.

*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: Ji Ma, Ivan Korotkov, Yinfei Yang, Keith Hall and Ryan McDonald. 2021. Zero-shot Neural Passage Retrieval via Domain-targeted Synthetic Question Generation. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume.

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

```bibtex
@inproceedings{ma2021zero,
  title = {{Zero-shot Neural Passage Retrieval via Domain-targeted Synthetic Question Generation}},
  author = {Ji Ma, Ivan Korotkov, Yinfei Yang, Keith Hall and Ryan McDonald},
  booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume},
  year = {2021},
  doi = {10.18653/v1/2021.eacl-main.92},
  url = {https://doi.org/10.18653/v1/2021.eacl-main.92}
}
```

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*Updated 24 October 2026*