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        <title>Selective Decoding for Cross-lingual Open Information Extraction</title>
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            <title>Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)</title>
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            <namePart type="family">Kondrak</namePart>
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    <abstract>Cross-lingual open information extraction is the task of distilling facts from the source language into representations in the target language. We propose a novel encoder-decoder model for this problem. It employs a novel selective decoding mechanism, which explicitly models the sequence labeling process as well as the sequence generation process on the decoder side. Compared to a standard encoder-decoder model, selective decoding significantly increases the performance on a Chinese-English cross-lingual open IE dataset by 3.87-4.49 BLEU and 1.91-5.92 F1. We also extend our approach to low-resource scenarios, and gain promising improvement.</abstract>
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%0 Conference Proceedings
%T Selective Decoding for Cross-lingual Open Information Extraction
%A Zhang, Sheng
%A Duh, Kevin
%A Van Durme, Benjamin
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F zhang-etal-2017-selective
%X Cross-lingual open information extraction is the task of distilling facts from the source language into representations in the target language. We propose a novel encoder-decoder model for this problem. It employs a novel selective decoding mechanism, which explicitly models the sequence labeling process as well as the sequence generation process on the decoder side. Compared to a standard encoder-decoder model, selective decoding significantly increases the performance on a Chinese-English cross-lingual open IE dataset by 3.87-4.49 BLEU and 1.91-5.92 F1. We also extend our approach to low-resource scenarios, and gain promising improvement.
%U https://aclanthology.org/I17-1084
%P 832-842

Markdown (Informal)

[Selective Decoding for Cross-lingual Open Information Extraction](https://aclanthology.org/I17-1084) (Zhang et al., IJCNLP 2017)

• Selective Decoding for Cross-lingual Open Information Extraction (Zhang et al., IJCNLP 2017)

ACL

• Sheng Zhang, Kevin Duh, and Benjamin Van Durme. 2017. Selective Decoding for Cross-lingual Open Information Extraction. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 832–842, Taipei, Taiwan. Asian Federation of Natural Language Processing.