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- W4317897789 abstract "Abstract We present Expected Statistic Regulariza tion (ESR), a novel regularization technique that utilizes low-order multi-task structural statistics to shape model distributions for semi- supervised learning on low-resource datasets. We study ESR in the context of cross-lingual transfer for syntactic analysis (POS tagging and labeled dependency parsing) and present several classes of low-order statistic functions that bear on model behavior. Experimentally, we evaluate the proposed statistics with ESR for unsupervised transfer on 5 diverse target languages and show that all statistics, when estimated accurately, yield improvements to both POS and LAS, with the best statistic improving POS by +7.0 and LAS by +8.5 on average. We also present semi-supervised transfer and learning curve experiments that show ESR provides significant gains over strong cross-lingual-transfer-plus-fine-tuning baselines for modest amounts of label data. These results indicate that ESR is a promising and complementary approach to model-transfer approaches for cross-lingual parsing.1" @default.
- W4317897789 created "2023-01-25" @default.
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- W4317897789 date "2023-01-01" @default.
- W4317897789 modified "2023-09-30" @default.
- W4317897789 title "Improving Low-Resource Cross-lingual Parsing with Expected Statistic Regularization" @default.
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- W4317897789 doi "https://doi.org/10.1162/tacl_a_00537" @default.
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