# Dealing with Typos for BERT-based Passage Retrieval and Ranking

arXiv: https://arxiv.org/abs/2108.12139 · HTML: https://mlsanity.com/paper/2108.12139/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2021.

## Built on by (most cited first, 23 of 23)

- [Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation](https://mlsanity.com/paper/2206.10128/) (2022)
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- [CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos](https://mlsanity.com/paper/2204.00716/) (2022)
- [Towards Imperceptible Document Manipulations against Neural Ranking Models](https://mlsanity.com/paper/2305.01860/) (2023)
- [Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective](https://mlsanity.com/paper/2407.06992/) (2024)
- [Prompt Perturbations Reveal Human-Like Biases in Large Language Model Survey Responses](https://mlsanity.com/paper/2507.07188/) (2025)
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- [A Study on FGSM Adversarial Training for Neural Retrieval](https://mlsanity.com/paper/2301.10576/) (2023)
- [On the Robustness of Generative Retrieval Models: An Out-of-Distribution Perspective](https://mlsanity.com/paper/2306.12756/) (2023)
- [Selecting which Dense Retriever to use for Zero-Shot Search](https://mlsanity.com/paper/2309.09403/) (2023)
- [On the Impact of Speech Recognition Errors in Passage Retrieval for Spoken Question Answering](https://mlsanity.com/paper/2209.12944/) (2022)
- [Typo-Robust Representation Learning for Dense Retrieval](https://mlsanity.com/paper/2306.10348/) (2023)
- [Noise-Robust Dense Retrieval via Contrastive Alignment Post Training](https://mlsanity.com/paper/2304.03401/) (2023)
- [RETVec: Resilient and Efficient Text Vectorizer](https://mlsanity.com/paper/2302.09207/) (2023)
- [Defense of Adversarial Ranking Attack in Text Retrieval: Benchmark and Baseline via Detection](https://mlsanity.com/paper/2307.16816/) (2023)
- [Asyncval: A Toolkit for Asynchronously Validating Dense Retriever Checkpoints during Training](https://mlsanity.com/paper/2202.12510/) (2022)
- [LEA: Improving Sentence Similarity Robustness to Typos Using Lexical Attention Bias](https://mlsanity.com/paper/2307.02912/) (2023)
- [Beyond Hard Samples: Robust and Effective Grammatical Error Correction with Cycle Self-Augmenting](https://mlsanity.com/paper/2310.13321/) (2023)
- [A Multimodal Dense Retrieval Approach for Speech-Based Open-Domain Question Answering](https://mlsanity.com/paper/2409.13483/) (2024)
- [Mine and Refine: Optimizing Graded Relevance in E-commerce Semantic Search Retrieval](https://mlsanity.com/paper/2602.17654/) (2026)
- [On the Robustness of LLM-Based Dense Retrievers: A Systematic Analysis of Generalizability and Stability](https://mlsanity.com/paper/2604.16576/) (2026)
- [KOMBO: Korean Character Representations Based on the Combination Rules of Subcharacters](https://mlsanity.com/paper/2604.23948/) (2026)
- [Hypencoder Revisited: Reproducibility and Analysis of Non-Linear Scoring for First-Stage Retrieval](https://mlsanity.com/paper/2604.27037/) (2026)

## Abstract (arXiv)

Passage retrieval and ranking is a key task in open-domain question answering and information retrieval. Current effective approaches mostly rely on pre-trained deep language model-based retrievers and rankers. These methods have been shown to effectively model the semantic matching between queries and passages, also in presence of keyword mismatch, i.e. passages that are relevant to a query but do not contain important query keywords. In this paper we consider the Dense Retriever (DR), a passage retrieval method, and the BERT re-ranker, a popular passage re-ranking method. In this context, we formally investigate how these models respond and adapt to a specific type of keyword mismatch -- that caused by keyword typos occurring in queries. Through empirical investigation, we find that typos can lead to a significant drop in retrieval and ranking effectiveness. We then propose a simple typos-aware training framework for DR and BERT re-ranker to address this issue. Our experimental results on the MS MARCO passage ranking dataset show that, with our proposed typos-aware training, DR and BERT re-ranker can become robust to typos in queries, resulting in significantly improved effectiveness compared to models trained without appropriately accounting for typos.