
Data and Benchmarking
Research-ready datasets and benchmarks for interconnected living texts.
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New preprint. Peer review is the cornerstone of academic publishing -- yet NLP for peer review lacks solid data foundation and is over-focused on few select research communities. We introduce NLPeer: the first clearly licensed, large-scale, multi-domain resource for computational study of peer review, incl. two novel corpora of peer reviews from the NLP community. While we are preparing the data for release, take a look at the preprint!
New preprint. NLP studies text, but what counts as text and what gets discarded depends on the study. In our fresh preprint, we discuss the emerging inclusive approach to text in NLP, introduce a taxonomy of extended text, and propose a community-wide reporting schema to facilitate future discussion.
Paper accepted at EMNLP. Our paper "Yes-Yes-Yes" will appear in the Findings of EMNLP-2022! We propose a new ethics-, copyright- and confidentiality-aware data collection workflow for peer reviews and study how donation-based collection affects the data. While we polish the camera-ready, have a look at our preprint and repo!
Keynote at EPIA-2022. Iryna Gurevych will give a keynote on new frontiers in cross-document NLP at the 21st EPIA Conference on Artificial Intelligence.
New CL article. Our article "Revise and Resubmit" got published in Computational Linguistics. We propose a joint framework for cross-document modeling inspired by theoretical work in intertextuality, and instantiate it in the peer reviewing domain, resulting in a large new corpus. Find the article online to know more!
Team

Iryna Gurevych
Principal Investigator

Ilia Kuznetsov
Postdoc

Martin Tutek
Postdoc

Jan Buchmann
PhD Student

Nils Dycke
PhD Student

Max Eichler
PhD Student

Dennis Zyska
PhD Student

Qian Ruan
PhD Student
Funding


