Exploiting cognates for transfer learning in under-resourced languages is an exciting opportunity for language understanding tasks, including unsupervised machine translation, named entity recognition and information retrieval. Previous approaches mainly focused on supervised cognate detection tasks based on orthographic, phonetic or state-of-the-art contextual language models, which under-perform for most under-resourced languages. This paper proposes a novel language-agnostic weakly-supervised deep cognate detection framework for under-resourced languages using morphological knowledge from closely related languages. We train an encoder to gain morphological knowledge of a language and transfer the knowledge to perform unsupervised and weakly-supervised cognate detection tasks with and without the pivot language for the closely-related languages. While unsupervised, it overcomes the need for hand-crafted annotation of cognates. We performed experiments on different published cognate detection datasets across language families and observed not only significant improvement over the state-of-the-art but also our method outperformed the state-of-the-art supervised and unsupervised methods. Our model can be extended to a wide range of languages from any language family as it overcomes the requirement of the annotation of the cognate pairs for training. The code and dataset building scripts can be found at https://github.com/koustavagoswami/Weakly_supervised-Cognate_Detection

翻译：利用同源词进行低资源语言的迁移学习是语言理解任务（包括无监督机器翻译、命名实体识别和信息检索）中的一个令人兴奋的机遇。以往方法主要专注于基于拼写、语音或最先进的上下文语言模型的有监督同源词检测任务，这些方法在大多数低资源语言上表现不佳。本文提出了一种新颖的语言无关的弱监督深度同源词检测框架，它利用密切相关的语言中的形态知识服务于低资源语言。我们训练一个编码器来获取语言的形态知识，并将该知识迁移，以在有或无枢轴语言的情况下，对密切相关的语言执行无监督和弱监督的同源词检测任务。在无监督模式下，它克服了手工标注同源词的需求。我们在不同语系的多个已发布的同源词检测数据集上进行了实验，不仅观察到相对于最先进方法的显著改进，而且我们的方法也优于最先进的有监督和无监督方法。我们的模型可以扩展到任何语系中的广泛语言，因为它克服了训练所需的同源词对标注要求。代码和数据集构建脚本可在 https://github.com/koustavagoswami/Weakly_supervised-Cognate_Detection 获取。