Existing question answering (QA) systems owe much of their success to large,
high-quality training data. Such annotation efforts are costly, and the
difficulty compounds in the cross-lingual setting. Therefore, prior
cross-lingual QA work has focused on releasing evaluation datasets, and then
applying zero-shot methods as baselines. In this work, we propose a synthetic
data generation method for cross-lingual QA which leverages indirect
supervision from existing parallel corpora. Our method termed PAXQA
({P}rojecting {a}nnotations for cross-lingual ({x}) QA) decomposes
cross-lingual QA into two stages. In the first stage, we apply a question
generation (QG) model to the English side. In the second stage, we apply
annotation projection to translate both the questions and answers. To better
translate questions, we propose a novel use of lexically-constrained machine
translation, in which constrained entities are extracted from the parallel
bitexts. We release cross-lingual QA datasets across 4 languages, totaling 662K
QA examples. We then show that extractive QA models fine-tuned on these
datasets outperform both zero-shot and prior synthetic data generation models,
showing the sufficient quality of our generations. We find that the largest
performance gains are for cross-lingual directions with non-English questions
and English contexts. Ablation studies show that our dataset generation method
is relatively robust to noise from automatic word alignments.

本文提出了一种利用现有平行语料库进行间接监督的跨语言问答（QA）的合成数据生成方法，并使用词汇约束的机器翻译提高翻译质量，生成了跨越 4 种语言的 662K QA 样例数据集，并通过消融研究证明了该方法相对于自动单词对齐的噪声比较稳健。