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We present multilingual Pre-trained Machine Reader (mPMR), a novel method formultilingual machine reading comprehension (MRC)-style pre-training. mPMR aimsto guide multilingual pre-trained language models (mPLMs) to perform naturallanguage understanding (NLU) including both sequence classification and spanextraction in multiple languages. To achieve cross-lingual generalization whenonly source-language fine-tuning data is available, existing mPLMs solelytransfer NLU capability from a source language to target languages. Incontrast, mPMR allows the direct inheritance of multilingual NLU capabilityfrom the MRC-style pre-training to downstream tasks. Therefore, mPMR acquiresbetter NLU capability for target languages. mPMR also provides a unified solverfor tackling cross-lingual span extraction and sequence classification, therebyenabling the extraction of rationales to explain the sentence-pairclassification process.

mPMR: A Multilingual Pre-trained Machine Reader at Scale