before_revision: We propose a simple method to generate large amounts of multilingual question and answer pairs by a single generative model. These synthetic samples are then applied to augment the available gold multilingual ones to improve the performance of multilingual QA models on target languages. Our approach only requires existence of automatically translated samples from Englishto the target domain , thus removing the need for human annotations in the target languages  . Experimental results show our proposed approach achieves significant gains in a number of multilingual datasets .
after_revision: We propose a simple method to generate multilingual question and answer pairs on a large scale through the use of a single generative model. These synthetic samples can be used to improve the zero-shot performance of multilingual QA models on target languages. Our proposed multi-task training of the generative model only requires the training samples in English , thus removing the need for labeled samples in the target languages , making it applicable to far more languages than those with labeled data . Experimental results show our proposed approach achieves significant gains on several multilingual QA benchmarks, reducing the gap between zero-shot and supervised performance of QA models on various languages .
edit_actions: D
edit_actions: large amounts of
edit_actions: clarity
edit_actions: coherence clarity clarity
edit_actions: R
edit_actions: by a
edit_actions: on a large scale through the use of a
edit_actions: meaning-changed
edit_actions: meaning-changed style meaning-changed
edit_actions: R
edit_actions: are then applied to augment the available gold multilingual ones to improve the
edit_actions: can be used to improve the zero-shot
edit_actions: clarity
edit_actions: clarity clarity clarity
edit_actions: R
edit_actions: approach only requires existence of automatically translated samples from Englishto the target domain
edit_actions: proposed multi-task training of the generative model only requires the training samples in English
edit_actions: clarity
edit_actions: meaning-changed clarity clarity
edit_actions: R
edit_actions: human annotations
edit_actions: labeled samples
edit_actions: clarity
edit_actions: clarity clarity style
edit_actions: A
edit_actions: , making it applicable to far more languages than those with labeled data
edit_actions: meaning-changed
edit_actions: meaning-changed meaning-changed meaning-changed
edit_actions: R
edit_actions: in a number of multilingual datasets
edit_actions: on several multilingual QA benchmarks, reducing the gap between zero-shot and supervised performance of QA models on various languages
edit_actions: meaning-changed
edit_actions: meaning-changed style meaning-changed