Cross-Lingual Training For Multiple-Choice Question Answering

In this work we explore to what extent multilingual models can be trained for one language and applied to a different one for the task of Multiple Choice Question Answering. We employ the RACE dataset to fine-tune both a monolingual and a multilingual models and apply these models to another different collections in different languages. The results show that both monolingual and multilingual models can be zero-shot transferred to a different dataset in the same language maintaining its performance. Besides, the multilingual model still performs good when it is applied to a different target language. Additionally, we find that exams that are more difficult to humans are harder for machines too. Finally, we advance the state-of-the-art for the QA4MRE Entrance Exams dataset in several languages.