双向预翻译的多数投票法在双文本检索中的应用
Majority Voting with Bidirectional Pre-translation For Bitext Retrieval
摘要：
获得高质量的平行语料库对于训练NMT系统至关重要。然而，由于许多语言对缺乏足够的黄金标准训练数据，一种流行的方法是从两种语言的成对文档中挖掘所谓的“伪平行”句子。在本文中，我们概述了一些问题与当前的方法，提出计算经济的解决方案，这些问题，并证明成功的新方法的Tatoeba相似性搜索基准和下游任务，即NMT。我们揭示了资源相关因素（即给定语言有多少单语/双语数据）对双文本挖掘方法的最佳选择的影响，并与其他人观察到的经常使用的BUCC数据集的问题相呼应。我们公开了用于实验的代码和数据。
英文摘要：
Obtaining high-quality parallel corpora is of paramount importance for training NMT systems. However, as many language pairs lack adequate gold-standard training data, a popular approach has been to mine so-called "pseudo-parallel" sentences from paired documents in two languages. In this paper, we outline some problems with current methods, propose computationally economical solutions to those problems, and demonstrate success with novel methods on the Tatoeba similarity search benchmark and on a downstream task, namely NMT. We uncover the effect of resource-related factors (i.e. how much monolingual/bilingual data is available for a given language) on the optimal choice of bitext mining approach, and echo problems with the oft-used BUCC dataset that have been observed by others. We make the code and data used for our experiments publicly available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
