  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
