# Cross Domain Regularization for Neural Ranking Models Using Adversarial Learning

arXiv: https://arxiv.org/abs/1805.03403 · HTML: https://mlsanity.com/paper/1805.03403/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2018.

## Built on by (most cited first, 10 of 10)

- [Joint Multisided Exposure Fairness for Recommendation](https://mlsanity.com/paper/2205.00048/) (2022)
- [Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective](https://mlsanity.com/paper/2407.06992/) (2024)
- [Low-Resource Dense Retrieval for Open-Domain Question Answering: A Comprehensive Survey](https://mlsanity.com/paper/2208.03197/) (2022)
- [Studying Catastrophic Forgetting in Neural Ranking Models](https://mlsanity.com/paper/2101.06984/) (2021)
- [Augmenting Scientific Creativity with Retrieval across Knowledge Domains](https://mlsanity.com/paper/2206.01328/) (2022)
- [Unsupervised Domain Adaptation for Hate Speech Detection Using a Data Augmentation Approach](https://mlsanity.com/paper/2107.12866/) (2021)
- [DUQGen: Effective Unsupervised Domain Adaptation of Neural Rankers by Diversifying Synthetic Query Generation](https://mlsanity.com/paper/2404.02489/) (2024)
- [Learning List-Level Domain-Invariant Representations for Ranking](https://mlsanity.com/paper/2212.10764/) (2022)
- [Are We There Yet? A Decision Framework for Replacing Term Based Retrieval with Dense Retrieval Systems](https://mlsanity.com/paper/2206.12993/) (2022)
- [Adaptable Text Matching via Meta-Weight Regulator](https://mlsanity.com/paper/2204.12668/) (2022)

## Abstract (arXiv)

Unlike traditional learning to rank models that depend on hand-crafted features, neural representation learning models learn higher level features for the ranking task by training on large datasets. Their ability to learn new features directly from the data, however, may come at a price. Without any special supervision, these models learn relationships that may hold only in the domain from which the training data is sampled, and generalize poorly to domains not observed during training. We study the effectiveness of adversarial learning as a cross domain regularizer in the context of the ranking task. We use an adversarial discriminator and train our neural ranking model on a small set of domains. The discriminator provides a negative feedback signal to discourage the model from learning domain specific representations. Our experiments show consistently better performance on held out domains in the presence of the adversarial discriminator---sometimes up to 30% on precision@1.