# Zero-shot Neural Passage Retrieval via Domain-targeted Synthetic Question Generation

arXiv: https://arxiv.org/abs/2004.14503 · HTML: https://mlsanity.com/paper/2004.14503/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2020.

## Built on by (most cited first, 8 of 8)

- [Large Dual Encoders Are Generalizable Retrievers](https://mlsanity.com/paper/2112.07899/) (2021)
- [Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering](https://mlsanity.com/paper/2210.02627/) (2022)
- [Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representations](https://mlsanity.com/paper/2110.07581/) (2021)
- [Overview of BioASQ 2020: The eighth BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering](https://mlsanity.com/paper/2106.14618/) (2021)
- [What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data](https://mlsanity.com/paper/2106.06410/) (2021)
- [ReadProbe: A Demo of Retrieval-Enhanced Large Language Models to Support Lateral Reading](https://mlsanity.com/paper/2306.07875/) (2023)
- [Improving Transformer-Kernel Ranking Model Using Conformer and Query Term Independence](https://mlsanity.com/paper/2104.09393/) (2021)
- [Distilling Transformers for Neural Cross-Domain Search](https://mlsanity.com/paper/2108.03322/) (2021)

## Abstract (arXiv)

A major obstacle to the wide-spread adoption of neural retrieval models is that they require large supervised training sets to surpass traditional term-based techniques, which are constructed from raw corpora. In this paper, we propose an approach to zero-shot learning for passage retrieval that uses synthetic question generation to close this gap. The question generation system is trained on general domain data, but is applied to documents in the targeted domain. This allows us to create arbitrarily large, yet noisy, question-passage relevance pairs that are domain specific. Furthermore, when this is coupled with a simple hybrid term-neural model, first-stage retrieval performance can be improved further. Empirically, we show that this is an effective strategy for building neural passage retrieval models in the absence of large training corpora. Depending on the domain, this technique can even approach the accuracy of supervised models.