Leveraging Domain Adaptation and Data Augmentation to Improve Qur'anic
  IR in English and Arabic
Vera Pavlova
In this work, we approach the problem of Qur'anic information retrieval (IR)
in Arabic and English. Using the latest state-of-the-art methods in neural IR,
we research what helps to tackle this task more efficiently. Training retrieval
models requires a lot of data, which is difficult to obtain for training
in-domain. Therefore, we commence with training on a large amount of general
domain data and then continue training on in-domain data. To handle the lack of
in-domain data, we employed a data augmentation technique, which considerably
improved results in MRR@10 and NDCG@5 metrics, setting the state-of-the-art in
Qur'anic IR for both English and Arabic. The absence of an Islamic corpus and
domain-specific model for IR task in English motivated us to address this lack
of resources and take preliminary steps of the Islamic corpus compilation and
domain-specific language model (LM) pre-training, which helped to improve the
performance of the retrieval models that use the domain-specific LM as the
shared backbone. We examined several language models (LMs) in Arabic to select
one that efficiently deals with the Qur'anic IR task. Besides transferring
successful experiments from English to Arabic, we conducted additional
experiments with retrieval task in Arabic to amortize the scarcity of general
domain datasets used to train the retrieval models. Handling Qur'anic IR task
combining English and Arabic allowed us to enhance the comparison and share
valuable insights across models and languages.