Multimodal Pre-training Language Count and Zero-Shot XNLI Performance

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: How does varying the number of languages in multimodal pre-training affect the zero-shot performance of vision-language models on the XNLI benchmark when evaluated across typologically diverse languages?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.2/10.