  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. This work proposes a synthetic data
generation method for cross-lingual QA which leverages indirect supervision
from existing parallel corpora. Our method termed PAXQA (Projecting annotations
for cross-lingual (x) QA) decomposes cross-lingual QA into two stages. First,
we apply a question generation (QG) model to the English side. Second, 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 apply PAXQA to generate cross-lingual QA examples in 4 languages (662K
examples total), and perform human evaluation on a subset to create validation
and test splits. We then show that models fine-tuned on these datasets
outperform prior synthetic data generation models over several extractive QA
datasets. The largest performance gains are for 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,
showing the sufficient quality of our generations. To facilitate follow-up
work, we release our code and datasets at https://github.com/manestay/paxqa .
