Romanian Question Answering Using Transformer Based Neural Networks
Question answering is the task of predicting answers for questions based on a context paragraph. It has become especially important, as the large amounts of textual data available online requires not only gathering information but also the task of findings specific answers to specific questions. In this work, we present experiments evaluated on the XQuAD-ro question answering dataset that has been recently published based on the translation of the SQuAD dataset into Romanian. Our bestperforming model, Romanian fine-tuned BERT, achieves an F1 score of 0.80 and an EM score of 0.73. We show that fine-tuning the model with the addition of the Romanian translation slightly increases the evaluation metrics.
 J. H. Clark, E. Choi, M. Collins, D. Garrette, T. Kwiatkowski, V. Nikolaev, and J. Palomaki. TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages. Transactions of the Association for Computational Linguistics, 8:454–470, 2020.
 A. Conneau, G. Lample, R. Rinott, A. Williams, S. R. Bowman, H. Schwenk, and V. Stoyanov. XNLI: Evaluating Cross-lingual Sentence Representations. arXiv preprint arXiv:1809.05053, 2018.
 Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
 S. D. Dumitrescu, P. Rebeja, B. Lorincz, M. Gaman, A. Avram, M. Ilie, A. Pruteanu, A. Stan, L. Rosia, C. Iacobescu, et al. LiRo: Benchmark and leaderboard for Romanian language tasks. 2021.
 P. Gupta and V. Gupta. A Survey of Text Question Answering Techniques. International Journal of Computer Applications, 53(4), 2012.
 L. Hirschman and R. Gaizauskas. Natural Language Question Answering: The View from Here. natural language engineering, 7(4):275–300, 2001.
 J. Hu, S. Ruder, A. Siddhant, G. Neubig, O. Firat, and M. Johnson. XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization. In International Conference on Machine Learning, pages 4411–4421. PMLR, 2020.
 A. Iftene, D. Trandabat, M. Husarciuc, and M. A. Moruz. Question Answering on Romanian, English and French Languages. In CLEF (notebook papers/LABs/workshops), 2010.
 C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. arXiv preprint arXiv:1910.10683, 2019.
 P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang. SQuAD: 100,000+ Questions for Machine Comprehension of Text. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2383–2392, Austin, Texas, Nov. 2016. Association for Computational Linguistics.
 A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin. Attention Is All You Need. In Advances in neural information processing systems, pages 5998–6008, 2017.
 A. Wang, Y. Pruksachatkun, N. Nangia, A. Singh, J. Michael, F. Hill, O. Levy, and S. R. Bowman. SuperGLUE: A stickier benchmark for general-purpose language understanding systems. arXiv preprint arXiv:1905.00537, 2019.
 A. Wang, A. Singh, J. Michael, F. Hill, O. Levy, and S. R. Bowman. Glue: A multi-task benchmark and analysis platform for natural language understanding. arXiv preprint arXiv:1804.07461, 2018.
 Y. Wu, M. Schuster, Z. Chen, Q. V. Le, M. Norouzi, W. Macherey, M. Krikun, Y. Cao, Q. Gao, K. Macherey, et al. Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. arXiv preprint arXiv:1609.08144, 2016.
 L. Xue, A. Barua, N. Constant, R. Al-Rfou, S. Narang, M. Kale, A. Roberts, and C. Raffel. ByT5: Towards a token-free future with pre-trained byte-to-byte models. arXiv preprint arXiv:2105.13626, 2021.
 L. Xue, N. Constant, A. Roberts, M. Kale, R. Al-Rfou, A. Siddhant, A. Barua, and C. Raffel. mT5: A massively multilingual pre-trained text-to-text transformer. arXiv preprint arXiv:2010.11934, 2020.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
When the article is accepted for publication, I, as the author and representative of the coauthors, hereby agree to transfer to Studia Universitatis Babes-Bolyai, Series Informatica, all rights, including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author specifically retain: the right to make further copies of all or part of the published article for my use in classroom teaching; the right to reuse all or part of this material in a review or in a textbook of which I am the author; the right to make copies of the published work for internal distribution within the institution that employs me.