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Halo: Learning Semantics-Aware Representations for Cross-Lingual Information Extraction

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 نشر من قبل Hongyuan Mei
 تاريخ النشر 2018
  مجال البحث الهندسة المعلوماتية
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Cross-lingual information extraction (CLIE) is an important and challenging task, especially in low resource scenarios. To tackle this challenge, we propose a training method, called Halo, which enforces the local region of each hidden state of a neural model to only generate target tokens with the same semantic structure tag. This simple but powerful technique enables a neural model to learn semantics-aware representations that are robust to noise, without introducing any extra parameter, thus yielding better generalization in both high and low resource settings.



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