activity
20182022
most citedBridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions

10 citations · 11 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL20221 cited

On the Effectiveness of Compact Biomedical Transformers

Omid Rohanian, Mohammadmahdi Nouriborji, Samaneh Kouchaki +1

Language models pre-trained on biomedical corpora, such as BioBERT, have recently shown promising results on downstream biomedical tasks. Many existing pre-trained models, on the o…

cs.CL2022

Nowruz at SemEval-2022 Task 7: Tackling Cloze Tests with Transformers and Ordinal Regression

Mohammadmahdi Nouriborji, Omid Rohanian, David Clifton

This paper outlines the system using which team Nowruz participated in SemEval 2022 Task 7 Identifying Plausible Clarifications of Implicit and Underspecified Phrases for both subt…

cs.LG2022

Privacy-aware Early Detection of COVID-19 through Adversarial Training

Omid Rohanian, Samaneh Kouchaki, Andrew Soltan +4

Early detection of COVID-19 is an ongoing area of research that can help with triage, monitoring and general health assessment of potential patients and may reduce operational stra…

cs.CL201910 cited

Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions

Omid Rohanian, Shiva Taslimipoor, Samaneh Kouchaki +2

We introduce a new method to tag Multiword Expressions (MWEs) using a linguistically interpretable language-independent deep learning architecture. We specifically target discontin…

cs.CL2018

SHOMA at Parseme Shared Task on Automatic Identification of VMWEs: Neural Multiword Expression Tagging with High Generalisation

Shiva Taslimipoor, Omid Rohanian

This paper presents a language-independent deep learning architecture adapted to the task of multiword expression (MWE) identification. We employ a neural architecture comprising o…