Comparison of Spell-Checking Algorithms in Dual-Encoder Retrieval Accuracy on NaturalQuestions

Dense retrieval is becoming one of the standard approaches for document and passage ranking. The dual-encoder architecture is widely adopted for scoring question-passage pairs due to its efficiency and high performance. Typically, dense retrieval models are evaluated on clean and curated datasets. However, when deployed in real-life applications, these models encounter noisy user-generated text. That said, the performance of state-of-the-art dense retrievers can substantially deteriorate when exposed to noisy text. In this work, we study the robustness of dense retrievers against typos in the

Research goal: How does the integration of different spell-checking algorithms (e.g., Hunspell, BERT-based, or phonetic) compare in terms of their impact on the retrieval accuracy of dual-encoder models on the NaturalQuestions benchmark?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.5/10.