SVCCA analysis of a 103-language translation model shows encoder representations cluster by linguistic family, diverge by target language, and high-resource or related languages are more robust to fine-tuning.
Continuous multilinguality with language vectors
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abstract
Most existing models for multilingual natural language processing (NLP) treat language as a discrete category, and make predictions for either one language or the other. In contrast, we propose using continuous vector representations of language. We show that these can be learned efficiently with a character-based neural language model, and used to improve inference about language varieties not seen during training. In experiments with 1303 Bible translations into 990 different languages, we empirically explore the capacity of multilingual language models, and also show that the language vectors capture genetic relationships between languages.
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Investigating Multilingual NMT Representations at Scale
SVCCA analysis of a 103-language translation model shows encoder representations cluster by linguistic family, diverge by target language, and high-resource or related languages are more robust to fine-tuning.