This work focuses on the rapid development of linguistic annotation tools for
resource-poor languages. We experiment several cross-lingual annotation
projection methods using Recurrent Neural Networks (RNN) models. The
distinctive feature of our approach is that our multilingual word
representation requires only a parallel corpus between the source and target
language. More precisely, our method has the following characteristics: (a) it
does not use word alignment information, (b) it does not assume any knowledge
about foreign languages, which makes it applicable to a wide range of
resource-poor languages, (c) it provides truly multilingual taggers. We
investigate both uni- and bi-directional RNN models and propose a method to
include external information (for instance low level information from POS) in
the RNN to train higher level taggers (for instance, super sense taggers). We
demonstrate the validity and genericity of our model by using parallel corpora
(obtained by manual or automatic translation). Our experiments are conducted to
induce cross-lingual POS and super sense taggers.

本文旨在研究对于资源贫乏的语言的快速语言注释工具的发展，我们采用递归神经网络模型实验了多种跨语言注释映射方法。我们提出了一种真正的多语言标记器方法，并通过使用平行语料库证实了其有效性和通用性。

使用双向递归神经网络引导多语言文本分析工具

Inducing Multilingual Text Analysis Tools Using Bidirectional Recurrent  Neural Networks

Open domain relation extraction systems identify relation and argument
phrases in a sentence without relying on any underlying schema. However,
current state-of-the-art relation extraction systems are available only for
English because of their heavy reliance on linguistic tools such as
part-of-speech taggers and dependency parsers. We present a cross-lingual
annotation projection method for language independent relation extraction. We
evaluate our method on a manually annotated test set and present results on
three typologically different languages. We release these manual annotations
and extracted relations in 61 languages from Wikipedia.

本研究提出了一种跨语言标注投影方法，用于无需依赖单一语言学工具的开放域关系提取系统，同时发布了来自维基百科中 61 种语言的手动注释和提取的关系。