281 citations · 652 across the 50 of their papers we have counts for
5 papers · 1 filter
Neuron-level Interpretation of Deep NLP Models: A Survey
Hassan Sajjad, Nadir Durrani, Fahim Dalvi
The proliferation of deep neural networks in various domains has seen an increased need for interpretability of these models. Preliminary work done along this line and papers that…
What do End-to-End Speech Models Learn about Speaker, Language and Channel Information? A Layer-wise and Neuron-level Analysis
Shammur Absar Chowdhury, Nadir Durrani, Ahmed Ali
Deep neural networks are inherently opaque and challenging to interpret. Unlike hand-crafted feature-based models, we struggle to comprehend the concepts learned and how they inter…
How transfer learning impacts linguistic knowledge in deep NLP models?
Nadir Durrani, Hassan Sajjad, Fahim Dalvi
Transfer learning from pre-trained neural language models towards downstream tasks has been a predominant theme in NLP recently. Several researchers have shown that deep NLP models…
Fine-grained Interpretation and Causation Analysis in Deep NLP Models
Hassan Sajjad, Narine Kokhlikyan, Fahim Dalvi +1
This paper is a write-up for the tutorial on "Fine-grained Interpretation and Causation Analysis in Deep NLP Models" that we are presenting at NAACL 2021. We present and discuss th…
Effect of Post-processing on Contextualized Word Representations
Hassan Sajjad, Firoj Alam, Fahim Dalvi +1
Post-processing of static embedding has beenshown to improve their performance on both lexical and sequence-level tasks. However, post-processing for contextualized embeddings is a…