How to Probe Sentence Embeddings in Low-Resource Languages: On Structural Design Choices for Probing Task Evaluation

Sentence encoders map sentences to real valued vectors for use in downstream\napplications. To peek into these representations - e.g., to increase\ninterpretability of their results - probing tasks have been designed which\nquery them for linguistic knowledge. However, designing probing tasks for\nlesser-resourced languages is tricky, because these often lack large-scale\nannotated data or (high-quality) dependency parsers as a prerequisite of\nprobing task design in English. To investigate how to probe sentence embeddings\nin such cases, we investigate sensitivity of probing task results to structural\ndesign choices, conducting the first such large scale study. We show that\ndesign choices like size of the annotated probing dataset and type of\nclassifier used for evaluation do (sometimes substantially) influence probing\noutcomes. We then probe embeddings in a multilingual setup with design choices\nthat lie in a 'stable region', as we identify for English, and find that\nresults on English do not transfer to other languages. Fairer and more\ncomprehensive sentence-level probing evaluation should thus be carried out on\nmultiple languages in the future.\n