Before revision:  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:  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 on several multilingual QA benchmarks, reducing the gap between zero-shot and supervised performance of QA models on various languages .