  Vision-Language Pre-training (VLP) has advanced the performance of many
vision-language tasks, such as image-text retrieval, visual entailment, and
visual reasoning. The pre-training mostly utilizes lexical databases and image
queries in English. Previous work has demonstrated that the pre-training in
English does not transfer well to other languages in a zero-shot setting.
However, multilingual pre-trained language models (MPLM) have excelled at a
variety of single-modal language tasks. In this paper, we propose a simple yet
efficient approach to adapt VLP to unseen languages using MPLM. We utilize a
cross-lingual contextualized token embeddings alignment approach to train text
encoders for non-English languages. Our approach does not require image input
and primarily uses machine translation, eliminating the need for target
language data. Our evaluation across three distinct tasks (image-text
retrieval, visual entailment, and natural language visual reasoning)
demonstrates that this approach outperforms the state-of-the-art multilingual
vision-language models without requiring large parallel corpora. Our code is
available at https://github.com/Yasminekaroui/CliCoTea.

Computation and Language