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Found 11 papers, 7 papers with code
SemEval-2024 Task 1: Semantic Textual Relatedness for African and Asian Languages
We present the first shared task on Semantic Textual Relatedness (STR).
Citation Amnesia: NLP and Other Academic Fields Are in a Citation Age Recession
This study examines the tendency to cite older work across 20 fields of study over 43 years (1980--2023).
SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 14 Languages
Exploring and quantifying semantic relatedness is central to representing language.
Collaboration or Corporate Capture? Quantifying NLP's Reliance on Industry Artifacts and Contributions
That these are industry models is noteworthy.
Benchmarking bias: Expanding clinical AI model card to incorporate bias reporting of social and non-social factors
Clinical AI model reporting cards should be expanded to incorporate a broad bias reporting of both social and non-social factors.
We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fields
We analyzed ~77k NLP papers, ~3. 1m citations from NLP papers to other papers, and ~1. 8m citations from other papers to NLP papers.
The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research
Recent advances in deep learning methods for natural language processing (NLP) have created new business opportunities and made NLP research critical for industry development.
What Makes Sentences Semantically Related: A Textual Relatedness Dataset and Empirical Study
We show that human intuition regarding relatedness of sentence pairs is highly reliable, with a repeat annotation correlation of 0. 84.
Examining the rhetorical capacities of neural language models
In this paper, we propose a method that quantitatively evaluates the rhetorical capacities of neural LMs.
Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings
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 on clinical tasks.
Cross-Lingual Sentiment Analysis Without (Good) Translation
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 translation systems.
