Automatic Spanish Translation of the SQuAD Dataset for Multilingual Question Answering

TL;DR

This paper presents a method to automatically translate the SQuAD dataset into Spanish, enabling the training of high-performing Spanish question answering systems and establishing new benchmarks for cross-lingual QA performance.

Contribution

The authors introduce the TAR method for translating SQuAD to Spanish and demonstrate its effectiveness by training state-of-the-art Spanish QA models using this dataset.

Findings

Achieved 68.1 F1 on Spanish MLQA benchmark

Achieved 77.6 F1 and 61.8 EM on Spanish XQuAD benchmark

First large-scale Spanish QA training dataset from SQuAD

Abstract

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.