An Item Response Theory Framework for Persuasion

no code implementations • Findings (NAACL) 2022 • Anastassia Kornilova, Daniel Argyle, Vladimir Eidelman

In this paper, we apply Item Response Theory, popular in education and political science research, to the analysis of argument persuasiveness in language.

Persuasiveness

Polylingual Tree-Based Topic Models for Translation Domain Adaptation

no code implementations • ACL 2014 • Yuening Hu, Ke Zhai, Vladimir Eidelman, Jordan Boyd-Graber

Domain Adaptation Information Retrieval +3

Towards Efficient Large-Scale Feature-Rich Statistical Machine Translation

no code implementations • WS 2013 • Vladimir Eidelman, Ke wu, Ferhan Ture, Philip Resnik, Jimmy Lin

Machine Translation Translation

Mr. MIRA: Open-Source Large-Margin Structured Learning on MapReduce

no code implementations • ACL 2013 • Vladimir Eidelman, Ke wu, Ferhan Ture, Philip Resnik, Jimmy Lin

Machine Translation Part-Of-Speech Tagging

Online Relative Margin Maximization for Statistical Machine Translation

no code implementations • ACL 2013 • Vladimir Eidelman, Yuval Marton, Philip Resnik

Machine Translation Structured Prediction +1

Unsupervised Feature-Rich Clustering

no code implementations • COLING 2012 • Vladimir Eidelman

Clustering Sentiment Analysis +1

Topic Models for Dynamic Translation Model Adaptation

no code implementations • ACL 2012 • Vladimir Eidelman, Jordan Boyd-Graber, Philip Resnik

Language Modelling Machine Translation +2

Optimization Strategies for Online Large-Margin Learning in Machine Translation

no code implementations • WS 2012 • Vladimir Eidelman

Machine Translation Structured Prediction +1