Multilingual Transfer Learning for QA Using Translation as Data Augmentation

Understand

Prior work on multilingual question answering has mostly focused on using large multilingual pre-trained language models (LM) to perform zero-shot language-wise learning: train a QA model on English and test on other languages.

• In this work, we explore strategies that improve cross-lingual transfer by bringing the multilingual embeddings closer in the semantic space.
• Our first strategy augments the original English training data with machine translation-generated data.
• This results in a corpus of multilingual silver-labeled QA pairs that is 14 times larger than the original training set.