limited-data

Paper Accepted to IJCAI 2021

A paper by CopeNLU author is accepted to appear at IJCAI 2021. The paper studies how to perform complex claim verification on naturally occurring political claims with multiple hops over evidence chunks. Multi-Hop Fact Checking of Political Claims. Wojciech Ostrowski, Arnav Arora, Pepa Atanasova, Isabelle Augenstein.

2 Papers Accepted to ACL 2021

2 papers by CopeNLU authors are accepted to appear at ACL 2021. One paper is on interpretability, examining how sparsity affects our ability to use attention as an explainability tool; whereas the other one is on scientific document understanding, introducing a new dataset for the task of cite-worthiness detection in scientific articles. Is Sparse Attention more Interpretable? Clara Meister, Stefan Lazov, Isabelle Augenstein, Ryan Cotterell. CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding.

Paper Accepted to EACL 2021

A paper by CopeNLU author is accepted to appear at EACL 2021. The paper aims to bridge the gap between high- and low-resource languages by investigating to what degree cross-lingual models share structural information about languages. Does Typological Blinding Impede Cross-Lingual Sharing?. Johannes Bjerva, Isabelle Augenstein.

Learning with Limited Labelled Data

Learning with limited labelled data, including multi-task learning, weakly supervised and zero-shot learning