GranCATs: Cross-Lingual Enhancement through Granularity-Specific Contrastive Adapters

Multilingual language models (MLLMs) have demonstrated remarkable success in various cross-lingual downstream tasks, facilitating the transfer of knowledge across numerous languages, whereas this transfer is not universally effective. Our study reveals that while existing MLLMs like mBERT can capturephrase-level alignments across the language families, they struggle to effectively capturesentence-level andparagraph-level alignments. To address this limitation, we propose GranCATs, Granularity-specific Contrastive AdapTers. We collect a new dataset that observes each sample at three distinct levels of granularity and employ contrastive learning as a pre-training task to train GranCATs on this dataset. Our objective is to enhance MLLMs' adaptation to a broader range of cross-lingual tasks by equipping them with improved capabilities to capture global information at different levels of granularity. Extensive experiments show that MLLMs with GranCATs yield significant performance advancements across various language tasks with different text granularities, including entity alignment, relation extraction, sentence classification and retrieval, and question-answering. These results validate the effectiveness of our proposed GranCATs in enhancing cross-lingual alignments across various text granularities and effectively transferring this knowledge to downstream tasks.