19 citations · 24 across the 9 of their papers we have counts for
9 papers
Integrating SAINT with Tree-Based Models: A Case Study in Employee Attrition Prediction
Adil Derrazi, Javad Pourmostafa Roshan Sharami
Employee attrition presents a major challenge for organizations, increasing costs and reducing productivity. Predicting attrition accurately enables proactive retention strategies,…
Toward domain-specific machine translation and quality estimation systems
Javad Pourmostafa Roshan Sharami
Machine Translation (MT) and Quality Estimation (QE) perform well in general domains but degrade under domain mismatch. This dissertation studies how to adapt MT and QE systems to…
Improving Medical Waste Classification with Hybrid Capsule Networks
Bennet van den Broek, Javad Pourmostafa Roshan Sharami
The improper disposal and mismanagement of medical waste pose severe environmental and public health risks, contributing to greenhouse gas emissions and the spread of infectious di…
Guiding In-Context Learning of LLMs through Quality Estimation for Machine Translation
Javad Pourmostafa Roshan Sharami, Dimitar Shterionov, Pieter Spronck
The quality of output from large language models (LLMs), particularly in machine translation (MT), is closely tied to the quality of in-context examples (ICEs) provided along with…
Tailoring Domain Adaptation for Machine Translation Quality Estimation
Javad Pourmostafa Roshan Sharami, Dimitar Shterionov, Frédéric Blain +4
While quality estimation (QE) can play an important role in the translation process, its effectiveness relies on the availability and quality of training data. For QE in particular…
A Systematic Analysis of Vocabulary and BPE Settings for Optimal Fine-tuning of NMT: A Case Study of In-domain Translation
J. Pourmostafa Roshan Sharami, D. Shterionov, P. Spronck
The effectiveness of Neural Machine Translation (NMT) models largely depends on the vocabulary used at training; small vocabularies can lead to out-of-vocabulary problems -- large…
Evaluating the Effectiveness of Pre-trained Language Models in Predicting the Helpfulness of Online Product Reviews
Ali Boluki, Javad Pourmostafa Roshan Sharami, Dimitar Shterionov
Businesses and customers can gain valuable information from product reviews. The sheer number of reviews often necessitates ranking them based on their potential helpfulness. Howev…
Evaluating performance of hybrid quantum optimization algorithms for MAXCUT Clustering using IBM runtime environment
Daniel Beaulieu, Anh Pham
Quantum algorithms can be used to perform unsupervised machine learning tasks like data clustering by mapping the distance between data points to a graph optimization problem (i.e.…
DeepSentiPers: Novel Deep Learning Models Trained Over Proposed Augmented Persian Sentiment Corpus
Javad PourMostafa Roshan Sharami, Parsa Abbasi Sarabestani, Seyed Abolghasem Mirroshandel
This paper focuses on how to extract opinions over each Persian sentence-level text. Deep learning models provided a new way to boost the quality of the output. However, these arch…