We studied the task of multiple-choice reading comprehension for low-resource languages, using a newly collected Bulgarian corpus with 2,633 questions from matriculation exams for twelfth grade in history and biology, and online exams in history without explanatory contexts.
we designed an end-to-end approach, on top of a multilingual BERT model (Devlin et al., 2019), which we fine-tuned on large-scale English reading comprehension corpora, and open-domain commonsense knowledge sources (Wikipedia).