65 citations · 76 across the 4 of their papers we have counts for
5 papers
CoDA21: Evaluating Language Understanding Capabilities of NLP Models With Context-Definition Alignment
Lütfi Kerem Senel, Timo Schick, Hinrich Schütze
Pretrained language models (PLMs) have achieved superhuman performance on many benchmarks, creating a need for harder tasks. We introduce CoDA21 (Context Definition Alignment), a c…
Does He Wink or Does He Nod? A Challenging Benchmark for Evaluating Word Understanding of Language Models
Lutfi Kerem Senel, Hinrich Schütze
Recent progress in pretraining language models on large corpora has resulted in large performance gains on many NLP tasks. These large models acquire linguistic knowledge during pr…
Imparting Interpretability to Word Embeddings while Preserving Semantic Structure
Lutfi Kerem Senel, Ihsan Utlu, Furkan Şahinuç +2
As an ubiquitous method in natural language processing, word embeddings are extensively employed to map semantic properties of words into a dense vector representation. They captur…
Semantic Structure and Interpretability of Word Embeddings
Lutfi Kerem Senel, Ihsan Utlu, Veysel Yucesoy +2
Dense word embeddings, which encode semantic meanings of words to low dimensional vector spaces have become very popular in natural language processing (NLP) research due to their…
Statistically Segregated k-Space Sampling for Accelerating Multiple-Acquisition MRI
L Kerem Senel, Toygan Kilic, Alper Gungor +5
A central limitation of multiple-acquisition magnetic resonance imaging (MRI) is the degradation in scan efficiency as the number of distinct datasets grows. Sparse recovery techni…