@InProceedings{zhang-duh-vandurme:2017:I17-1,
  author    = {Zhang, Sheng  and  Duh, Kevin  and  Van Durme, Benjamin},
  title     = {Selective Decoding for Cross-lingual Open Information Extraction},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {832--842},
  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.},
  url       = {http://www.aclweb.org/anthology/I17-1084}
}