Lazy Low-Resource Coreference Resolution: a Study on Leveraging Black-Box Translation Tools

Semere Kiros Bitew, Johannes Deleu, Chris Develder, Thomas Demeester
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引用次数: 1

Abstract

Large annotated corpora for coreference resolution are available for few languages. For machine translation, however, strong black-box systems exist for many languages. We empirically explore the appealing idea of leveraging such translation tools for bootstrapping coreference resolution in languages with limited resources. Two scenarios are analyzed, in which a large coreference corpus in a high-resource language is used for coreference predictions in a smaller language, i.e., by machine translating either the training corpus or the test data. In our empirical evaluation of coreference resolution using the two scenarios on several medium-resource languages, we find no improvement over monolingual baseline models. Our analysis of the various sources of error inherent to the studied scenarios, reveals that in fact the quality of contemporary machine translation tools is the main limiting factor.
懒惰的低资源共同参考解析:利用黑盒翻译工具的研究
用于共同参考解析的大型带注释的语料库可用于几种语言。然而,对于机器翻译来说,许多语言都存在强大的黑盒系统。我们从经验上探讨了在资源有限的语言中利用这种翻译工具来引导共指解析的吸引人的想法。分析了两种场景,其中高资源语言的大型共参考语料库用于较小语言的共参考预测,即通过机器翻译训练语料库或测试数据。在我们对几种中等资源语言使用两种场景的共同参考分辨率的实证评估中,我们发现单语言基线模型没有改进。我们对所研究情景中固有的各种错误来源的分析表明,实际上当代机器翻译工具的质量是主要的限制因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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