281 citations · 902 across the 36 of their papers we have counts for
9 papers · 1 filter
Analyzing Individual Neurons in Pre-trained Language Models
Nadir Durrani, Hassan Sajjad, Fahim Dalvi +1
While a lot of analysis has been carried to demonstrate linguistic knowledge captured by the representations learned within deep NLP models, very little attention has been paid tow…
Fighting the COVID-19 Infodemic in Social Media: A Holistic Perspective and a Call to Arms
Firoj Alam, Fahim Dalvi, Shaden Shaar +8
With the outbreak of the COVID-19 pandemic, people turned to social media to read and to share timely information including statistics, warnings, advice, and inspirational stories.…
Similarity Analysis of Contextual Word Representation Models
John M. Wu, Yonatan Belinkov, Hassan Sajjad +3
This paper investigates contextual word representation models from the lens of similarity analysis. Given a collection of trained models, we measure the similarity of their interna…
Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society
Firoj Alam, Shaden Shaar, Fahim Dalvi +14
With the emergence of the COVID-19 pandemic, the political and the medical aspects of disinformation merged as the problem got elevated to a whole new level to become the first glo…
CrisisBench: Benchmarking Crisis-related Social Media Datasets for Humanitarian Information Processing
Firoj Alam, Hassan Sajjad, Muhammad Imran +1
Time-critical analysis of social media streams is important for humanitarian organizations for planing rapid response during disasters. The \textit{crisis informatics} research com…
Analyzing Redundancy in Pretrained Transformer Models
Fahim Dalvi, Hassan Sajjad, Nadir Durrani +1
Transformer-based deep NLP models are trained using hundreds of millions of parameters, limiting their applicability in computationally constrained environments. In this paper, we…
On the Effect of Dropping Layers of Pre-trained Transformer Models
Hassan Sajjad, Fahim Dalvi, Nadir Durrani +1
Transformer-based NLP models are trained using hundreds of millions or even billions of parameters, limiting their applicability in computationally constrained environments. While…
A Clustering Framework for Lexical Normalization of Roman Urdu
Abdul Rafae Khan, Asim Karim, Hassan Sajjad +2
Roman Urdu is an informal form of the Urdu language written in Roman script, which is widely used in South Asia for online textual content. It lacks standard spelling and hence pos…
Compressing Large-Scale Transformer-Based Models: A Case Study on BERT
Prakhar Ganesh, Yao Chen, Xin Lou +6
Pre-trained Transformer-based models have achieved state-of-the-art performance for various Natural Language Processing (NLP) tasks. However, these models often have billions of pa…