no code implementations • 7 Nov 2023 • Manas Mohanty, Tanya Roosta, Peyman Passban
Deep neural networks (DNNs) have improved NLP tasks significantly, but training and maintaining such networks could be costly.
no code implementations • NAACL 2022 • Peyman Passban, Tanya Roosta, Rahul Gupta, Ankit Chadha, Clement Chung
Training mixed-domain translation models is a complex task that demands tailored architectures and costly data preparation techniques.
no code implementations • COLING 2022 • Joyce Zheng, Mehdi Rezagholizadeh, Peyman Passban
To solve this problem, position embeddings are defined exclusively for each time step to enrich word information.
no code implementations • 12 Dec 2021 • Tanya Roosta, Peyman Passban, Ankit Chadha
These new components are placed in between original layers.
1 code implementation • Findings (ACL) 2021 • Ehsan Kamalloo, Mehdi Rezagholizadeh, Peyman Passban, Ali Ghodsi
We exploit a semi-supervised approach based on KD to train a model on augmented data.
no code implementations • 18 Apr 2021 • Krtin Kumar, Peyman Passban, Mehdi Rezagholizadeh, Yiu Sing Lau, Qun Liu
Embedding matrices are key components in neural natural language processing (NLP) models that are responsible to provide numerical representations of input tokens.\footnote{In this paper words and subwords are referred to as \textit{tokens} and the term \textit{embedding} only refers to embeddings of inputs.}
no code implementations • 17 Apr 2021 • Kira A. Selby, Yinong Wang, Ruizhe Wang, Peyman Passban, Ahmad Rashid, Mehdi Rezagholizadeh, Pascal Poupart
Despite recent monumental advances in the field, many Natural Language Processing (NLP) models still struggle to perform adequately on noisy domains.
no code implementations • Findings (EMNLP) 2021 • Peyman Passban, Puneeth S. M. Saladi, Qun Liu
There is a large body of work in the NMT literature on analyzing the behavior of conventional models for the problem of noise but Transformers are relatively understudied in this context.
no code implementations • 27 Dec 2020 • Peyman Passban, Yimeng Wu, Mehdi Rezagholizadeh, Qun Liu
Knowledge distillation is considered as a training and compression strategy in which two neural networks, namely a teacher and a student, are coupled together during training.
2 code implementations • EMNLP 2020 • Yimeng Wu, Peyman Passban, Mehdi Rezagholizade, Qun Liu
With the growth of computing power neural machine translation (NMT) models also grow accordingly and become better.
no code implementations • COLING 2018 • Peyman Passban, Andy Way, Qun Liu
A morphologically complex word (MCW) is a hierarchical constituent with meaning-preserving subunits, so word-based models which rely on surface forms might not be powerful enough to translate such structures.
no code implementations • NAACL 2018 • Peyman Passban, Qun Liu, Andy Way
Recently, neural machine translation (NMT) has emerged as a powerful alternative to conventional statistical approaches.
no code implementations • 17 Apr 2018 • Alberto Poncelas, Dimitar Shterionov, Andy Way, Gideon Maillette de Buy Wenniger, Peyman Passban
A prerequisite for training corpus-based machine translation (MT) systems -- either Statistical MT (SMT) or Neural MT (NMT) -- is the availability of high-quality parallel data.
no code implementations • COLING 2016 • Peyman Passban, Qun Liu, Andy Way
PBSMT engines by default provide four probability scores in phrase tables which are considered as the main set of bilingual features.