Multi-Positive Contrastive Learning for Zero-Shot Cross-Lingual Dense Retrieval on the XMRC Benchmark

Dense retrieval has become the new paradigm in passage retrieval. Despite its effectiveness on typo-free queries, it is not robust when dealing with queries that contain typos. Current works on improving the typo-robustness of dense retrievers combine (i) data augmentation to obtain the typoed queries during training time with (ii) additional robustifying subtasks that aim to align the original, typo-free queries with their typoed variants. Even though multiple typoed variants are available as positive samples per query, some methods assume a single positive sample and a set of negative ones p

Research goal: How does multi-positive contrastive learning impact the zero-shot cross-lingual retrieval accuracy of dense retrievers on the XMRC benchmark compared to standard InfoNCE loss?

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