Beyond English-Only Reading Comprehension: Experiments in Zero-shot Multilingual Transfer for Bulgarian

By Momchil Hardalov, Ivan Koychev, Preslav Nakov. 2019

22 citations recorded by the source corpus.

Recently, reading comprehension models achieved near-human performance on large-scale datasets such as SQuAD, CoQA, MS Macro, RACE, etc. This is largely due to the release of pre-trained contextualized representations such as BERT and ELMo, which can be fine-tuned for the target task. Despite those advances and the creation of more challenging datasets, most of the work is still done for English. Here, we study the effectiveness of multilingual BERT fine-tuned on large-scale English datasets for reading comprehension (e.g., for RACE), and we apply it to Bulgarian multiple-choice reading comprehension. We propose a new dataset containing 2,221 questions from matriculation exams for twelfth grade in various subjects —history, biology, geography and philosophy—, and 412 additional questions from online quizzes in history. While the quiz authors gave no relevant context, we incorporate knowledge from Wikipedia, retrieving documents matching the combination of question + each answer option. Moreover, we experiment with different indexing and pre-training strategies. The evaluation results show accuracy of 42.23%, which is well above the baseline of 24.89%.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing, Question Answering & Reading Comprehension

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Citations in the corpus

The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants (2023, background): “Monolingual closed-book MRC datasets now exist in Arabic (Mozannar et al., 2019), Bulgarian (Hardalov et al., 2019), French (d’Hoffschmidt et al., 2020), German (Möller et al., 2021), Hindi (Anuranjana et al., 2019; Gupta et al., 2018), Italian (Croce et al., 2018), Russian (Efimov et al., 2020; Sha”