# End-to-End Retrieval in Continuous Space

arXiv: https://arxiv.org/abs/1811.08008 · HTML: https://mlsanity.com/paper/1811.08008/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2018.

## Built on by (most cited first, 30 of 46)

- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
- [Unsupervised Dense Information Retrieval with Contrastive Learning](https://mlsanity.com/paper/2112.09118/) (2021)
- [Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models](https://mlsanity.com/paper/2108.08877/) (2021)
- [Large Dual Encoders Are Generalizable Retrievers](https://mlsanity.com/paper/2112.07899/) (2021)
- [Transformer Memory as a Differentiable Search Index](https://mlsanity.com/paper/2202.06991/) (2022)
- [Promptagator: Few-shot Dense Retrieval From 8 Examples](https://mlsanity.com/paper/2209.11755/) (2022)
- [XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation](https://mlsanity.com/paper/2104.07412/) (2021)
- [User-LLM: Efficient LLM Contextualization with User Embeddings](https://mlsanity.com/paper/2402.13598/) (2024)
- [How Does Generative Retrieval Scale to Millions of Passages?](https://mlsanity.com/paper/2305.11841/) (2023)
- [PhotoChat: A Human-Human Dialogue Dataset with Photo Sharing Behavior for Joint Image-Text Modeling](https://mlsanity.com/paper/2108.01453/) (2021)
- [Contrastive language and vision learning of general fashion concepts](https://mlsanity.com/paper/2204.03972/) (2022)
- [A Statutory Article Retrieval Dataset in French](https://mlsanity.com/paper/2108.11792/) (2021)
- [Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learning](https://mlsanity.com/paper/2208.14565/) (2022)
- [Talk, Don't Write: A Study of Direct Speech-Based Image Retrieval](https://mlsanity.com/paper/2104.01894/) (2021)
- [Exploring Dual Encoder Architectures for Question Answering](https://mlsanity.com/paper/2204.07120/) (2022)
- [Perspectives and Prospects on Transformer Architecture for Cross-Modal Tasks with Language and Vision](https://mlsanity.com/paper/2103.04037/) (2021)
- [MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network](https://mlsanity.com/paper/2106.07352/) (2021)
- [ARL2: Aligning Retrievers for Black-box Large Language Models via Self-guided Adaptive Relevance Labeling](https://mlsanity.com/paper/2402.13542/) (2024)
- [Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks](https://mlsanity.com/paper/2301.12847/) (2023)
- [SamToNe: Improving Contrastive Loss for Dual Encoder Retrieval Models with Same Tower Negatives](https://mlsanity.com/paper/2306.02516/) (2023)
- [UGSL: A Unified Framework for Benchmarking Graph Structure Learning](https://mlsanity.com/paper/2308.10737/) (2023)
- [LAIT: Efficient Multi-Segment Encoding in Transformers with Layer-Adjustable Interaction](https://mlsanity.com/paper/2305.19585/) (2023)
- [ColBERT-XM: A Modular Multi-Vector Representation Model for Zero-Shot Multilingual Information Retrieval](https://mlsanity.com/paper/2402.15059/) (2024)
- [Learning Federated Representations and Recommendations with Limited Negatives](https://mlsanity.com/paper/2108.07931/) (2021)
- [Exploring the Practicality of Generative Retrieval on Dynamic Corpora](https://mlsanity.com/paper/2305.18952/) (2023)
- [Bridging Continuous and Discrete Spaces: Interpretable Sentence Representation Learning via Compositional Operations](https://mlsanity.com/paper/2305.14599/) (2023)
- [Revealing the Blind Spot of Sentence Encoder Evaluation by HEROS](https://mlsanity.com/paper/2306.05083/) (2023)
- [Policy-Gradient Training of Language Models for Ranking](https://mlsanity.com/paper/2310.04407/) (2023)
- [Query Drift Compensation: Enabling Compatibility in Continual Learning of Retrieval Embedding Models](https://mlsanity.com/paper/2506.00037/) (2025)
- [Adam: Dense Retrieval Distillation with Adaptive Dark Examples](https://mlsanity.com/paper/2212.10192/) (2022)

## Abstract (arXiv)

Most text-based information retrieval (IR) systems index objects by words or phrases. These discrete systems have been augmented by models that use embeddings to measure similarity in continuous space. But continuous-space models are typically used just to re-rank the top candidates. We consider the problem of end-to-end continuous retrieval, where standard approximate nearest neighbor (ANN) search replaces the usual discrete inverted index, and rely entirely on distances between learned embeddings. By training simple models specifically for retrieval, with an appropriate model architecture, we improve on a discrete baseline by 8% and 26% (MAP) on two similar-question retrieval tasks. We also discuss the problem of evaluation for retrieval systems, and show how to modify existing pairwise similarity datasets for this purpose.