Papers by Michael Kranzlein

2 papers
Making Heads and Tails of Models with Marginal Calibration for Sparse Tagsets (2021.findings-emnlp)

Copied to clipboard

Challenge: despite high accuracy, modern neural networks can still suffer from severe miscalibration.
Approach: They propose to use tag frequency grouping to measure calibration error in different frequency bands to reduce error.
Outcome: The proposed techniques reduce calibration error across the marginal distribution for two existing sequence taggers.
CuRIAM: Corpus Re Interpretation and Metalanguage in U.S. Supreme Court Opinions (2024.lrec-main)

Copied to clipboard

Challenge: judicial opinions use language to comment on or draw attention to other language . a recent case involving a federal anti-discrimination law requires that justices determine the meaning of just one word or phrase in a specific context.
Approach: They identify 9 categories prominent in metalinguistic discussions, including key terms, definitions, and different kinds of sources.
Outcome: The results show that the annotated concepts are well-defined and frequent, and that they differ between majority, concurring, and dissenting opinions.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations