Multimodal Pre-training Effects on Zero-Shot Cross-Lingual Transfer for Dravidian Languages

Abstract

Abstract: 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 Question

How does multimodal pre-training with image-text pairs affect zero-shot cross-lingual transfer F1 scores for low-resource Dravidian languages on the XTREME benchmark compared to text-only BERT models?

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