Active Selection of Data for Machine Translation

目前的机器翻译(MT)方法依赖于大量的文本数据。然而，对于许多语言或专业词汇表，即使是主要语言，也无法获得大量数据。这个项目从一个相当幼稚的人类双语信息者那里获得了双语数据。即使在大量数据和训练有素的语言学家没有的情况下，双语使用者也可以使用一种语言。语料库导航器使用来自语言类型学的知识来选择对自动学习机器翻译规则最有价值的数据片段。语料库导航器采用主动学习，通过从译者那里获取数据来更新其状态。目前正在测试两个假设：如果是正确的数据，机器翻译系统可以使用更少的数据；通过语言知识指导的主动学习过程，可以获得正确的数据。目前政府运营的机器翻译评估为这些假设提供了试验床。对不同数据集上训练的机器翻译系统的输出进行比较，以确定假设是否正确。作为概念验证，语料库导航器的初步原型正在生产中。该项目将使在没有大量文本资源的情况下构建机器翻译系统变得更容易。将测试的语言可能包括Inupiaq、孟加拉语、泰语、乌尔都语、乌兹别克语和提格里亚语。语料库导航的输出是一个平行的、词对齐的语料库，带有语义特征结构标注。这些数据将提供给其他研究人员。

Current methods for machine translation (MT) rely on large amounts of text data. However, large data is not available for many languages or for specialized vocabularies even in major languages. This project elicits bilingual data from a fairly naive human bilingual informant.Bilingual speakers are available for a language even when large data and trained linguists are not. A Corpus Navigator uses knowledge from language typology to choose the pieces of data that are most valuable for automatic learning of MT rules. The Corpus Navigator employs active learning in the sense that its state is updated by eliciting data from a human translator.Two hypotheses are being tested: an MT system can get by with less data if it is the right data, and that the right data can be acquired through an active learning process guided by linguistic knowledge.Current government-run MT evaluations provide a testbed for these hypotheses. The outputs of MT systems trained on different data sets are compared in order to determine whether the hypotheses are correct. An initial prototype Corpus Navigator is being produced as a proof-of-concept. This project will make it easier to build MT systems in situations where large text resources are not available. Languages that will be tested may include Inupiaq, Bengali, Thai, Urdu, Uzbek, and Tigrinia.The output of Corpus Navigation is a parallel, word-aligned corpus annotated with a semantic feature structure. This data will be available to other researchers.