  The availability of large, high-quality datasets has been one of the main
drivers of recent progress in question answering (QA). Such annotated datasets
however are difficult and costly to collect, and rarely exist in languages
other than English, rendering QA technology inaccessible to underrepresented
languages. An alternative to building large monolingual training datasets is to
leverage pre-trained language models (PLMs) under a few-shot learning setting.
Our approach, QAmeleon, uses a PLM to automatically generate multilingual data
upon which QA models are trained, thus avoiding costly annotation. Prompt
tuning the PLM for data synthesis with only five examples per language delivers
accuracy superior to translation-based baselines, bridges nearly 60% of the gap
between an English-only baseline and a fully supervised upper bound trained on
almost 50,000 hand labeled examples, and always leads to substantial
improvements compared to fine-tuning a QA model directly on labeled examples in
low resource settings. Experiments on the TyDiQA-GoldP and MLQA benchmarks show
that few-shot prompt tuning for data synthesis scales across languages and is a
viable alternative to large-scale annotation.
