Applying Multilingual Models to Question Answering (QA)
This work addresses the problem of multilingual question-answering for researchers and practitioners, but it appears incremental as it applies existing methods to new languages without major innovations.
The study evaluated monolingual and multilingual language models on question-answering tasks across English, Finnish, and Japanese, focusing on answerability detection and answer extraction using IOB tagging, and assessed Multilingual BERT for cross-language zero-shot learning.
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.