activity
20172023
most citedHurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings

29 citations · 67 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2023★ 17 cited

The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research

Mohamed Abdalla, Jan Philip Wahle, Terry Ruas +4

Recent advances in deep learning methods for natural language processing (NLP) have created new business opportunities and made NLP research critical for industry development. As o…

cs.CL2021★ 6 cited

What Makes Sentences Semantically Related: A Textual Relatedness Dataset and Empirical Study

Mohamed Abdalla, Krishnapriya Vishnubhotla, Saif M. Mohammad

The degree of semantic relatedness of two units of language has long been considered fundamental to understanding meaning. Additionally, automatically determining relatedness has m…

cs.CL2020

Examining the rhetorical capacities of neural language models

Zining Zhu, Chuer Pan, Mohamed Abdalla +1

Recently, neural language models (LMs) have demonstrated impressive abilities in generating high-quality discourse. While many recent papers have analyzed the syntactic aspects enc…

cs.CL2020★ 29 cited

Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings

Haoran Zhang, Amy X. Lu, Mohamed Abdalla +2

In this work, we examine the extent to which embeddings may encode marginalized populations differently, and how this may lead to a perpetuation of biases and worsened performance…

cs.CL2017★ 15 cited

Cross-Lingual Sentiment Analysis Without (Good) Translation

Mohamed Abdalla, Graeme Hirst

Current approaches to cross-lingual sentiment analysis try to leverage the wealth of labeled English data using bilingual lexicons, bilingual vector space embeddings, or machine tr…