Question Answering (QA) has enticed the interest of NLP community in recent years. NLP enthusiasts are engineering new Models and fine-tuning the existing ones that can give out answers for the posed questions. The deep neural network models are found to perform exceptionally on QA tasks, but these models are also data intensive. For instance, BERT has outperformed many of its contemporary contenders on SQuAD dataset. In this work, we attempt at solving the closed domain reading comprehension Question Answering task on QRCD (Qur’anic Reading Comprehension Dataset) to extract an answer span from the provided passage, using BERT as a baseline model. We improved the model’s output by applying regularization techniques like weight-decay and data augmentation. Using different strategies we had 0.59% and 0.31% partial Reciprocal Ranking (pRR) on development and testing data splits respectively.

In this paper, we present a system for transliterating the Arabic-based script of Urdu to a Roman transliteration scheme. The system is integrated into a larger system consisting of a morphology module, implemented via finite state technologies, and a computational LFG grammar of Urdu that was developed with the grammar development platform XLE (Crouch et al. 2008). Our long-term goal is to handle Hindi alongside Urdu; the two languages are very similar with respect to syntax and lexicon and hence, one grammar can be used to cover both languages. However, they are not similar concerning the script -- Hindi is written in Devanagari, while Urdu uses an Arabic-based script. By abstracting away to a common Roman transliteration scheme in the respective transliterators, our system can be enabled to handle both languages in parallel. In this paper, we discuss the pipeline architecture of the Urdu-Roman transliterator, mention several linguistic and orthographic issues and present the integration of the transliterator into the LFG parsing system.