Recently, multilingual question answering became a crucial research topic,
and it is receiving increased interest in the NLP community. However, the
unavailability of large-scale datasets makes it challenging to train
multilingual QA systems with performance comparable to the English ones. In
this work, we develop the Translate Align Retrieve (TAR) method to
automatically translate the Stanford Question Answering Dataset (SQuAD) v1.1 to
Spanish. We then used this dataset to train Spanish QA systems by fine-tuning a
Multilingual-BERT model. Finally, we evaluated our QA models with the recently
proposed MLQA and XQuAD benchmarks for cross-lingual Extractive QA.
Experimental results show that our models outperform the previous
Multilingual-BERT baselines achieving the new state-of-the-art value of 68.1 F1
points on the Spanish MLQA corpus and 77.6 F1 and 61.8 Exact Match points on
the Spanish XQuAD corpus. The resulting, synthetically generated SQuAD-es v1.1
corpora, with almost 100% of data contained in the original English version, to
the best of our knowledge, is the first large-scale QA training resource for
Spanish.

本文提出 Translate Align Retrieve (TAR) 方法，通过将 Stanford Question Answering Dataset（SQuAD）v1.1 自动翻译成西班牙语，创建了大规模的西班牙语 QA ​​training dataset。使用此数据集通过微调 Multilingual-BERT 模型训练了西班牙语 QA 系统，并在 MLQA 和 XQuAD 基准测试上进行了评估，结果表明该方法优于 Multilingual-BERT 基线，达到了新的最高 69.1 F1 分数。