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Cross-lingual Adaptation Using Universal Dependencies
ACM Transactions on Asian and Low-Resource Language Information Processing ( IF 2 ) Pub Date : 2021-05-26 , DOI: 10.1145/3448251
Nasrin Taghizadeh 1 , Heshaam Faili 2
Affiliation  

We describe a cross-lingual adaptation method based on syntactic parse trees obtained from the Universal Dependencies (UD), which are consistent across languages, to develop classifiers in low-resource languages. The idea of UD parsing is to capture similarities as well as idiosyncrasies among typologically different languages. In this article, we show that models trained using UD parse trees for complex NLP tasks can characterize very different languages. We study two tasks of paraphrase identification and relation extraction as case studies. Based on UD parse trees, we develop several models using tree kernels and show that these models trained on the English dataset can correctly classify data of other languages, e.g., French, Farsi, and Arabic. The proposed approach opens up avenues for exploiting UD parsing in solving similar cross-lingual tasks, which is very useful for languages for which no labeled data is available.

中文翻译:

使用通用依赖的跨语言适配

我们描述了一种基于从通用依赖关系 (UD) 获得的句法分析树的跨语言适应方法,该方法在语言之间是一致的,以开发低资源语言的分类器。UD 解析的想法是捕捉类型不同的语言之间的相似性和特质。在本文中,我们展示了针对复杂 NLP 任务使用 UD 解析树训练的模型可以表征非常不同的语言。我们研究了释义识别和关系提取这两个任务作为案例研究。基于 UD 解析树,我们使用树核开发了几个模型,并表明这些在英语数据集上训练的模型可以正确分类其他语言的数据,例如法语、波斯语和阿拉伯语。
更新日期:2021-05-26
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