Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension

Abstract

Multilingual pre-trained models could leverage the training data from a rich source language (such as English) to improve the performance on low resource languages. However, the transfer effectiveness on the multilingual Machine Reading Comprehension (MRC) task is substantially poorer than that for sentence classification tasks, mainly due to the requirement of MRC to detect the word level answer boundary. In this paper, we propose two auxiliary tasks to introduce additional phrase boundary supervision in the fine-tuning stage: