  We study the performance of monolingual and multilingual language models on
the task of question-answering (QA) on three diverse languages: English,
Finnish and Japanese. We develop models for the tasks of (1) determining if a
question is answerable given the context and (2) identifying the answer texts
within the context using IOB tagging. Furthermore, we attempt to evaluate the
effectiveness of a pre-trained multilingual encoder (Multilingual BERT) on
cross-language zero-shot learning for both the answerability and IOB sequence
classifiers.
