Cross-lingual Retrieval Performance of SimCSE and LaBSE under Character-Level Noise in Low-Resource Languages

Benefiting from transformer-based pre-trained language models, neural ranking models have made significant progress. More recently, the advent of multilingual pre-trained language models provides great support for designing neural cross-lingual retrieval models. However, due to unbalanced pre-training data in different languages, multilingual language models have already shown a performance gap between high and low-resource languages in many downstream tasks. And cross-lingual retrieval models built on such pre-trained models can inherit language bias, leading to suboptimal result for low-reso

Research goal: How does the MRR performance of SimCSE-trained dual-encoders compare to LaBSE when evaluated on cross-lingual retrieval tasks with varying levels of character-level noise injection in low-resource languages?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.2/10.