# Neural Ranking Models with Weak Supervision

arXiv: https://arxiv.org/abs/1704.08803 · HTML: https://mlsanity.com/paper/1704.08803/

## Key facts

- Cited by 61 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2017.

## Built on by (most cited first, 30 of 61)

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- [Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method](https://mlsanity.com/paper/2308.09861/) (2023)
- [MS MARCO: Benchmarking Ranking Models in the Large-Data Regime](https://mlsanity.com/paper/2105.04021/) (2021)
- [Universalizing Weak Supervision](https://mlsanity.com/paper/2112.03865/) (2021)
- [Trial2Vec: Zero-Shot Clinical Trial Document Similarity Search using Self-Supervision](https://mlsanity.com/paper/2206.14719/) (2022)
- [Enhancing Q&A Text Retrieval with Ranking Models: Benchmarking, fine-tuning and deploying Rerankers for RAG](https://mlsanity.com/paper/2409.07691/) (2024)
- [Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering](https://mlsanity.com/paper/2203.06942/) (2022)
- [LED: Lexicon-Enlightened Dense Retriever for Large-Scale Retrieval](https://mlsanity.com/paper/2208.13661/) (2022)
- [Improving Document Representations by Generating Pseudo Query Embeddings for Dense Retrieval](https://mlsanity.com/paper/2105.03599/) (2021)
- [Learning to Predict RNA Sequence Expressions from Whole Slide Images with Applications for Search and Classification](https://mlsanity.com/paper/2203.13997/) (2022)
- [Low-Resource Dense Retrieval for Open-Domain Question Answering: A Comprehensive Survey](https://mlsanity.com/paper/2208.03197/) (2022)
- [Multi-granular Adversarial Attacks against Black-box Neural Ranking Models](https://mlsanity.com/paper/2404.01574/) (2024)
- [Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models](https://mlsanity.com/paper/2102.00036/) (2021)
- [IncDSI: Incrementally Updatable Document Retrieval](https://mlsanity.com/paper/2307.10323/) (2023)
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- [Pre-Training Multi-Modal Dense Retrievers for Outside-Knowledge Visual Question Answering](https://mlsanity.com/paper/2306.16478/) (2023)
- [Traffic-Domain Video Question Answering with Automatic Captioning](https://mlsanity.com/paper/2307.09636/) (2023)
- [Studying Catastrophic Forgetting in Neural Ranking Models](https://mlsanity.com/paper/2101.06984/) (2021)
- [SamToNe: Improving Contrastive Loss for Dual Encoder Retrieval Models with Same Tower Negatives](https://mlsanity.com/paper/2306.02516/) (2023)
- [A Systematic Evaluation of Transfer Learning and Pseudo-labeling with BERT-based Ranking Models](https://mlsanity.com/paper/2103.03335/) (2021)
- [Mixed Attention Transformer for Leveraging Word-Level Knowledge to Neural Cross-Lingual Information Retrieval](https://mlsanity.com/paper/2109.02789/) (2021)
- [Mind the Gap: Cross-Lingual Information Retrieval with Hierarchical Knowledge Enhancement](https://mlsanity.com/paper/2112.13510/) (2021)
- [Dense Retrieval Adaptation using Target Domain Description](https://mlsanity.com/paper/2307.02740/) (2023)
- [Hypencoder: Hypernetworks for Information Retrieval](https://mlsanity.com/paper/2502.05364/) (2025)

## Abstract (arXiv)

Despite the impressive improvements achieved by unsupervised deep neural networks in computer vision and NLP tasks, such improvements have not yet been observed in ranking for information retrieval. The reason may be the complexity of the ranking problem, as it is not obvious how to learn from queries and documents when no supervised signal is available. Hence, in this paper, we propose to train a neural ranking model using weak supervision, where labels are obtained automatically without human annotators or any external resources (e.g., click data). To this aim, we use the output of an unsupervised ranking model, such as BM25, as a weak supervision signal. We further train a set of simple yet effective ranking models based on feed-forward neural networks. We study their effectiveness under various learning scenarios (point-wise and pair-wise models) and using different input representations (i.e., from encoding query-document pairs into dense/sparse vectors to using word embedding representation). We train our networks using tens of millions of training instances and evaluate it on two standard collections: a homogeneous news collection(Robust) and a heterogeneous large-scale web collection (ClueWeb). Our experiments indicate that employing proper objective functions and letting the networks to learn the input representation based on weakly supervised data leads to impressive performance, with over 13% and 35% MAP improvements over the BM25 model on the Robust and the ClueWeb collections. Our findings also suggest that supervised neural ranking models can greatly benefit from pre-training on large amounts of weakly labeled data that can be easily obtained from unsupervised IR models.