Adversarial Training Impact on Zero-Shot Cross-Lingual Retrieval Accuracy in Dual-Encoder Models

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 adversarial training with synthetic noise affect the zero-shot cross-lingual retrieval accuracy of dual-encoder models on the TyDiQA benchmark?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.3/10.