  Accuracy of English-language Question Answering (QA) systems has improved
significantly in recent years with the advent of Transformer-based models
(e.g., BERT). These models are pre-trained in a self-supervised fashion with a
large English text corpus and further fine-tuned with a massive English QA
dataset (e.g., SQuAD). However, QA datasets on such a scale are not available
for most of the other languages. Multi-lingual BERT-based models (mBERT) are
often used to transfer knowledge from high-resource languages to low-resource
languages. Since these models are pre-trained with huge text corpora containing
multiple languages, they typically learn language-agnostic embeddings for
tokens from different languages. However, directly training an mBERT-based QA
system for low-resource languages is challenging due to the paucity of training
data. In this work, we augment the QA samples of the target language using
translation and transliteration into other languages and use the augmented data
to fine-tune an mBERT-based QA model, which is already pre-trained in English.
Experiments on the Google ChAII dataset show that fine-tuning the mBERT model
with translations from the same language family boosts the question-answering
performance, whereas the performance degrades in the case of cross-language
families. We further show that introducing a contrastive loss between the
translated question-context feature pairs during the fine-tuning process,
prevents such degradation with cross-lingual family translations and leads to
marginal improvement. The code for this work is available at
https://github.com/gokulkarthik/mucot.
