Multimodal Intermediate-Task Training for Zero-Shot Cross-Lingual Transfer in Multilingual Models

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: What is the impact of multimodal intermediate-task training (e.g., combining text and images) on zero-shot cross-lingual transfer performance for multilingual models, as measured by accuracy on XTREME-R and alignment scores with vision-language models like CLIP?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.8/10.