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For a long time, WALS was used primarily by linguists to map language families. But today, AI researchers realize that if a model knows that Language A and Language B share the same typological features (e.g., both use postpositions instead of prepositions), the model can learn to translate between them better than between structurally distant languages. (quantified WALS) is a tool that measures these distances to boost machine translation accuracy. wals roberta sets top

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When fine-tuning RoBERTa for a new language (NER, Sentiment, Parsing), using WALS features as a prior helps. Rather than clustering by language family (which often fails), researchers show that clustering by yields higher accuracy. Rather than clustering by language family (which often

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