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Hassan Sajjad

41 papers hereh-index 384.9k citations100 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author8
  • middle author25
  • last author8

Across the 41 of 41 papers where every author was matched, so the position is known.

fields
  • cs.CL36
  • cs.IR2
  • cs.SI2
  • cs.LG1
same name
  • Hassan Sajjad — 17 papers, h 4
  • Hassan Sajjad — 9 papers, h 4
  • Hassan Sajjad — 6 papers, h 2
  • Hassan Sajjad — 5 papers, h 2
  • Hassan Sajjad — 2 papers, h 2
  • Hassan Sajjad — 1 paper, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20142023
most citedWhat do Neural Machine Translation Models Learn about Morphology?

281 citations · 902 across the 36 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.CL2021

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…

cs.CL2021

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…

cs.CL2021

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…

cs.CL2021★ 2 cited

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…

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