Improving Language and Modality Transfer in Translation by Character-level Modeling

• URL: http://arxiv.org/abs/2505.24561v1
• Date: Fri, 30 May 2025 13:16:08 GMT
• Title: Improving Language and Modality Transfer in Translation by Character-level Modeling
• Authors: Ioannis Tsiamas, David Dale, Marta R. Costa-jussà,
• Abstract summary: Current translation systems, despite being highly multilingual, cover only 5% of the world's languages.<n>We propose a character-based approach to improve adaptability to new languages and modalities.
• Score: 14.145120349133007
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current translation systems, despite being highly multilingual, cover only 5% of the world's languages. Expanding language coverage to the long-tail of low-resource languages requires data-efficient methods that rely on cross-lingual and cross-modal knowledge transfer. To this end, we propose a character-based approach to improve adaptability to new languages and modalities. Our method leverages SONAR, a multilingual fixed-size embedding space with different modules for encoding and decoding. We use a teacher-student approach with parallel translation data to obtain a character-level encoder. Then, using ASR data, we train a lightweight adapter to connect a massively multilingual CTC ASR model (MMS), to the character-level encoder, potentially enabling speech translation from 1,000+ languages. Experimental results in text translation for 75 languages on FLORES+ demonstrate that our character-based approach can achieve better language transfer than traditional subword-based models, especially outperforming them in low-resource settings, and demonstrating better zero-shot generalizability to unseen languages. Our speech adaptation, maximizing knowledge transfer from the text modality, achieves state-of-the-art results in speech-to-text translation on the FLEURS benchmark on 33 languages, surpassing previous supervised and cascade models, albeit being a zero-shot model with minimal supervision from ASR data.

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