05.11.2021
MCML at EMNLP 2021
Three Accepted Papers (2 Main, and 1 Workshop)
Conference on Empirical Methods in Natural Language Processing, Punta Cana, Dominican Republic, Nov 07-11, 2021
We are happy to announce that MCML researchers have contributed a total of 3 papers to EMNLP 2021: 2 Main, and 1 Workshop papers. Congrats to our researchers!
Main Track (2 papers)
A. Imani • M. J. Sabet • L. K. Senel • P. Dufter • F. Yvon • H. Schütze
Graph Algorithms for Multiparallel Word Alignment.
EMNLP 2021 - Conference on Empirical Methods in Natural Language Processing. Punta Cana, Dominican Republic, Nov 07-11, 2021. DOI
Graph Algorithms for Multiparallel Word Alignment.
EMNLP 2021 - Conference on Empirical Methods in Natural Language Processing. Punta Cana, Dominican Republic, Nov 07-11, 2021. DOI
N. Kassner • O. Tafjord • H. Schütze • P. Clark
BeliefBank: Adding Memory to a Pre-Trained Language Model for a Systematic Notion of Belief.
EMNLP 2021 - Conference on Empirical Methods in Natural Language Processing. Punta Cana, Dominican Republic, Nov 07-11, 2021. DOI
BeliefBank: Adding Memory to a Pre-Trained Language Model for a Systematic Notion of Belief.
EMNLP 2021 - Conference on Empirical Methods in Natural Language Processing. Punta Cana, Dominican Republic, Nov 07-11, 2021. DOI
Workshops (1 paper)
N. Kees • M. Fromm • E. Faerman • T. Seidl
Active Learning for Argument Strength Estimation.
Insights @EMNLP 2021 - 2nd Workshop on Insights from Negative Results at the Conference on Empirical Methods in Natural Language Processing. Punta Cana, Dominican Republic, Nov 07-11, 2021. DOI
Active Learning for Argument Strength Estimation.
Insights @EMNLP 2021 - 2nd Workshop on Insights from Negative Results at the Conference on Empirical Methods in Natural Language Processing. Punta Cana, Dominican Republic, Nov 07-11, 2021. DOI
Related
26.08.2026
Transparency for Global Health Aid
MCML PI Stefan Feuerriegel and his team use machine learning to uncover disparities in the global allocation of health aid.
25.08.2026
David Rügamer: We Need Uncertainty Quantification
MCML PI David Rügamer explains why uncertainty quantification is essential for building trustworthy AI and making informed decisions.
