Domain Adaptation Effects on Zero-Shot Cross-Lingual Transfer in Multilingual Multimodal Models

Abstract

Abstract: Multilingual Neural Machine Translation (NMT) excels in sharing knowledge across languages and transferring insights from high-resource languages to improve performance in low-resource languages. However, its performance lags in specific domains such as legal and medical. Previous works have focused on adding language-specific and domain-specific adapters to achieve domain adaptation. Although effective, these adapter-based methods only use domain data to train additional parameters, limiting the performance of multilingual NMT. In this paper, we propose CDSTX, a novel approach that achieves r

Research Question

How does domain adaptation during fine-tuning influence the zero-shot cross-lingual transfer performance of multilingual multimodal models on XTREME-R, compared to models without domain-specific adaptation?

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