Investigating the Effect of Parallel Data in the Cross-Lingual Transfer for Vision-Language Encoders

• URL: http://arxiv.org/abs/2504.21681v1
• Date: Wed, 30 Apr 2025 14:19:15 GMT
• Title: Investigating the Effect of Parallel Data in the Cross-Lingual Transfer for Vision-Language Encoders
• Authors: Andrei-Alexandru Manea, Jindřich Libovický,
• Abstract summary: Most pre-trained Vision-Language (VL) models and training data for the downstream tasks are only available in English.<n>We study the alternative approach: transferring an already trained encoder using parallel data.<n>Our results show that even machine-translated task data are the best on average, caption-like authentic parallel data outperformed it in some languages.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most pre-trained Vision-Language (VL) models and training data for the downstream tasks are only available in English. Therefore, multilingual VL tasks are solved using cross-lingual transfer: fine-tune a multilingual pre-trained model or transfer the text encoder using parallel data. We study the alternative approach: transferring an already trained encoder using parallel data. We investigate the effect of parallel data: domain and the number of languages, which were out of focus in previous work. Our results show that even machine-translated task data are the best on average, caption-like authentic parallel data outperformed it in some languages. Further, we show that most languages benefit from multilingual training.

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