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arXiv · 1805.08271

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

Hongyuan Mei·Sheng Zhang·Kevin Duh·Benjamin Van Durme
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Abstract

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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Hongyuan Mei, Sheng Zhang, Kevin Duh, Benjamin Van Durme. 2018-05-21. Halo: Learning Semantics-Aware Representations for Cross-Lingual Information Extraction. https://arxiv.org/abs/1805.08271

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