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Evaluating Human Pairwise Preference Judgments
Author(s) -
Mark Dras
Publication year - 2015
Publication title -
computational linguistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.314
H-Index - 98
eISSN - 1530-9312
pISSN - 0891-2017
DOI - 10.1162/coli_a_00222
Subject(s) - bespoke , computer science , preference , pairwise comparison , context (archaeology) , artificial intelligence , natural language processing , sample (material) , machine learning , statistics , mathematics , paleontology , chemistry , chromatography , political science , law , biology
Human evaluation plays an important role in NLP, often in the form of preference judgments. Although there has been some use of classical non-parametric and bespoke approaches to evaluating these sorts of judgments, there is an entire body of work on this in the context of sensory discrimination testing and the human judgments that are central to it, backed by rigorous statistical theory and freely available software, that NLP can draw on. We investigate one approach, Log-Linear Bradley-Terry models, and apply it to sample NLP data.9 page(s

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