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It is found that language distances provide a practical basis for equitable and performant transfer language selection, and are substantially reduced by training-free composite distances, and nearly eliminated by trained rankers.
This work proposes a simple yet effective adaptive sampling strategy that assigns trainable sampling probabilities to each language and employs an inner-outer optimization loop with a small overhead, leading to consistent performance improvements and, more importantly, distributing the gains across languages.
This work extends URIEL+ by introducing script vectors to represent writing system properties for 7,488 languages, integrating Glottolog to add 18,710 additional languages, and expanding lineage imputation for 26,449 languages by propagating typological and script features across genealogies.
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