Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models

• URL: http://arxiv.org/abs/2103.08849v2
• Date: Thu, 18 Mar 2021 17:40:09 GMT
• Title: Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models
• Authors: Po-Yao Huang, Mandela Patrick, Junjie Hu, Graham Neubig, Florian Metze and Alexander Hauptmann
• Abstract summary: We study zero-shot cross-lingual transfer of vision-language models. We propose a Transformer-based model that learns contextualized multilingual multimodal embeddings.
• Score: 144.85290716246533
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. Experiments on VTT show that our method significantly improves video search in non-English languages without additional annotations. Furthermore, when multilingual annotations are available, our method outperforms recent baselines by a large margin in multilingual text-to-video search on VTT and VATEX; as well as in multilingual text-to-image search on Multi30K. Our model and Multi-HowTo100M is available at http://github.com/berniebear/Multi-HT100M.

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