# Automatic Spanish Translation of the SQuAD Dataset for Multilingual Question Answering

arXiv: https://arxiv.org/abs/1912.05200 · HTML: https://mlsanity.com/paper/1912.05200/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2019.

## Built on by (most cited first, 30 of 35)

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- [An Efficient Multilingual Language Model Compression through Vocabulary Trimming](https://mlsanity.com/paper/2305.15020/) (2023)
- [PQuAD: A Persian Question Answering Dataset](https://mlsanity.com/paper/2202.06219/) (2022)
- [MergeDistill: Merging Pre-trained Language Models using Distillation](https://mlsanity.com/paper/2106.02834/) (2021)
- [A Practical Toolkit for Multilingual Question and Answer Generation](https://mlsanity.com/paper/2305.17416/) (2023)
- [Analyzing Zero-shot Cross-lingual Transfer in Supervised NLP Tasks](https://mlsanity.com/paper/2101.10649/) (2021)
- [On the Usability of Transformers-based models for a French Question-Answering task](https://mlsanity.com/paper/2207.09150/) (2022)
- [Do Sparse Autoencoders Generalize? A Case Study of Answerability](https://mlsanity.com/paper/2502.19964/) (2025)
- [FQuAD2.0: French Question Answering and knowing that you know nothing](https://mlsanity.com/paper/2109.13209/) (2021)
- [KazQAD: Kazakh Open-Domain Question Answering Dataset](https://mlsanity.com/paper/2404.04487/) (2024)
- [ALBETO and DistilBETO: Lightweight Spanish Language Models](https://mlsanity.com/paper/2204.09145/) (2022)
- [Explanatory Argument Extraction of Correct Answers in Resident Medical Exams](https://mlsanity.com/paper/2312.00567/) (2023)
- [RigoBERTa: A State-of-the-Art Language Model For Spanish](https://mlsanity.com/paper/2205.10233/) (2022)
- [PCoQA: Persian Conversational Question Answering Dataset](https://mlsanity.com/paper/2312.04362/) (2023)
- [PORTULAN ExtraGLUE Datasets and Models: Kick-starting a Benchmark for the Neural Processing of Portuguese](https://mlsanity.com/paper/2404.05333/) (2024)
- [TIGQA:An Expert Annotated Question Answering Dataset in Tigrinya](https://mlsanity.com/paper/2404.17194/) (2024)
- [Interpreting Indirect Answers to Yes-No Questions in Multiple Languages](https://mlsanity.com/paper/2310.13290/) (2023)
- [Question Answering and Question Generation for Finnish](https://mlsanity.com/paper/2211.13794/) (2022)
- [FairytaleQA Translated: Enabling Educational Question and Answer Generation in Less-Resourced Languages](https://mlsanity.com/paper/2406.04233/) (2024)
- [SynDARin: Synthesising Datasets for Automated Reasoning in Low-Resource Languages](https://mlsanity.com/paper/2406.14425/) (2024)
- [ACE-2005-PT: Corpus for Event Extraction in Portuguese](https://mlsanity.com/paper/2408.16928/) (2024)
- [Finnish SQuAD: A Simple Approach to Machine Translation of Span Annotations](https://mlsanity.com/paper/2501.05963/) (2025)
- [SkillNet-X: A Multilingual Multitask Model with Sparsely Activated Skills](https://mlsanity.com/paper/2306.16176/) (2023)
- [Language Model Knowledge Distillation for Efficient Question Answering in Spanish](https://mlsanity.com/paper/2312.04193/) (2023)
- [Building Efficient and Effective OpenQA Systems for Low-Resource Languages](https://mlsanity.com/paper/2401.03590/) (2024)
- [Synthetic Dataset Creation and Fine-Tuning of Transformer Models for Question Answering in Serbian](https://mlsanity.com/paper/2404.08617/) (2024)

## Abstract (arXiv)

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.