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        <title>Halo: Learning Semantics-Aware Representations for Cross-Lingual Information Extraction</title>
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        <namePart type="given">Hongyuan</namePart>
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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.</abstract>
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%0 Conference Proceedings
%T Halo: Learning Semantics-Aware Representations for Cross-Lingual Information Extraction
%A Mei, Hongyuan
%A Zhang, Sheng
%A Duh, Kevin
%A Van Durme, Benjamin
%Y Nissim, Malvina
%Y Berant, Jonathan
%Y Lenci, Alessandro
%S Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F mei-etal-2018-halo
%X 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.
%R 10.18653/v1/S18-2017
%U https://aclanthology.org/S18-2017
%U https://doi.org/10.18653/v1/S18-2017
%P 142-147

Markdown (Informal)

[Halo: Learning Semantics-Aware Representations for Cross-Lingual Information Extraction](https://aclanthology.org/S18-2017) (Mei et al., *SEM 2018)

• Halo: Learning Semantics-Aware Representations for Cross-Lingual Information Extraction (Mei et al., *SEM 2018)

ACL

• Hongyuan Mei, Sheng Zhang, Kevin Duh, and Benjamin Van Durme. 2018. Halo: Learning Semantics-Aware Representations for Cross-Lingual Information Extraction. In Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, pages 142–147, New Orleans, Louisiana. Association for Computational Linguistics.