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- W4386839860 abstract "Customizing machine translation models to comply with fine-grained attributes such as formality has seen tremendous progress recently. However, current approaches mostly rely on at least some supervised data with attribute annotation. Data scarcity therefore remains a bottleneck to democratizing such customization possibilities to a wider range of languages, lower-resource ones in particular. Given recent progress in pretrained massively multilingual translation models, we use them as a foundation to transfer the attribute controlling capabilities to languages without supervised data. In this work, we present a comprehensive analysis of transferring attribute controllers based on a pretrained NLLB-200 model. We investigate both training- and inference-time control techniques under various data scenarios, and uncover their relative strengths and weaknesses in zero-shot performance and domain robustness. We show that both paradigms are complementary, as shown by consistent improvements on 5 zero-shot directions. Moreover, a human evaluation on a real low-resource language, Bengali, confirms our findings on zero-shot transfer to new target languages. The code is $href{https://github.com/dannigt/attribute-controller-transfer}{text{here}}$." @default.
- W4386839860 created "2023-09-19" @default.
- W4386839860 creator A5046084081 @default.
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- W4386839860 date "2023-09-15" @default.
- W4386839860 modified "2023-09-27" @default.
- W4386839860 title "How Transferable are Attribute Controllers on Pretrained Multilingual Translation Models?" @default.
- W4386839860 doi "https://doi.org/10.48550/arxiv.2309.08565" @default.
- W4386839860 hasPublicationYear "2023" @default.
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