Sept. 14, 2022, 1:15 a.m. | Caleb Ziems, Jiaao Chen, Camille Harris, Jessica Anderson, Diyi Yang

cs.CL updates on arXiv.org arxiv.org

English Natural Language Understanding (NLU) systems have achieved great
performances and even outperformed humans on benchmarks like GLUE and
SuperGLUE. However, these benchmarks contain only textbook Standard American
English (SAE). Other dialects have been largely overlooked in the NLP
community. This leads to biased and inequitable NLU systems that serve only a
sub-population of speakers. To understand disparities in current models and to
facilitate more dialect-competent NLU systems, we introduce the VernAcular
Language Understanding Evaluation (VALUE) benchmark, a challenging variant …

arxiv nlu understanding value

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Principal Data Engineering Manager

@ Microsoft | Redmond, Washington, United States

Machine Learning Engineer

@ Apple | San Diego, California, United States