Multimodal Pre-training for Enhanced Zero-Shot Cross-Lingual Transfer in XTREME-R

This paper studies zero-shot cross-lingual transfer of vision-language models. Specifically, we focus on multilingual text-to-video search and propose a Transformer-based model that learns contextualized multilingual multimodal embeddings. Under a zero-shot setting, we empirically demonstrate that performance degrades significantly when we query the multilingual text-video model with non-English sentences. To address this problem, we introduce a multilingual multimodal pre-training strategy, and collect a new multilingual instructional video dataset (MultiHowTo100M) for pre-training. Experimen

Research goal: Can the robustness of zero-shot cross-lingual transfer in XTREME-R be improved by incorporating multimodal pre-training (e.g., CLIP or BLIP), and how does this affect benchmark scores compared to text-only models like mT5?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.6/10.