Javad Pourmostafa Roshan Sharami

Bio

I'm a PhD candidate/lecturer at the Department of Cognitive Science and Artificial Intelligence of Tilburg University.

Before joining Tilburg University, I was a postgraduate member of the Guilan NLP Group and a part-time lecturer at the Computer Engineering Department of the University of Guilan. I earned my BSc and MSc degrees in Software Engineering in 2016 and 2019, respectively. 

My research mainly focuses on natural language processing and deep learning. 

Some topics of interest to me are:

• Machine Translation (MT)
• Multilingual and multi-domain domain adaptation for neural MT
• Quality Estimation
• Data Selection
• In-context Learning in LLMs for translation tasks

Courses

• Programming with C/C++
• Master thesis/Data Science in Action

Top publications

1. Tailoring Domain Adaptation for Machine Translation Quality Estimation

   Sharami, J. P. R., Shterionov, D., Blain, F., Vanmassenhove, E., Sisto, M. D., Emmery, C., & Spronck, P. (Accepted/In press). Tailoring Domain Adaptation for Machine Translation Quality Estimation. In Proceedings of the 24th Annual Conference of the European Association for Machine Translation

2. A Quality Estimation and Quality Evaluation Tool for the Translation …

   Murgolo, E., Sharami, J. P. R., & Shterionov, D. (2022). A Quality Estimation and Quality Evaluation Tool for the Translation Industry. In Proceedings of the 23rd Annual Conference of the European Association for Machine Translation (pp. 305-306). European Association for Machine Translation. https://aclanthology.org/2022.eamt-1.43

3. Selecting Parallel In-domain Sentences for Neural Machine Translation…

   Pourmostafa Roshan Sharami, J., Shterionov, D., & Spronck, P. (2021). Selecting Parallel In-domain Sentences for Neural Machine Translation Using Monolingual Texts. Computational Linguistics in the Netherlands Journal, 11, 213-230.

4. DeepSentiPers - Novel Deep Learning Models Trained Over Proposed Augm…

   PourMostafa Roshan Sharami, J., Sarabestani, P. A., & Mirroshandel, S. A. (2020, Apr 1). DeepSentiPers - Novel Deep Learning Models Trained Over Proposed Augmented Persian Sentiment Corpus. https://arxiv.org/abs/2004.05328

5. Evaluating the Effectiveness of Pre-trained Language Models in Predic…

   Boluki, A., Pourmostafa Roshan Sharami, J., & Shterionov, D. (2024). Evaluating the Effectiveness of Pre-trained Language Models in Predicting the Helpfulness of Online Product Reviews. In K. Arai (Ed.), Intelligent Systems and Applications (pp. 15-35). Springer Nature Switzerland AG.