NLP for living texts, in context.

Natural language processing (NLP) is a subfield of AI and computational linguistics dedicated to the analysis and generation of natural texts. For most of its history, NLP has seen text as a static, isolated entity – yet real digital texts change over time, and are written and read in the context of other texts. To enable a new generation of NLP research and applications, the InterText initiative develops novel NLP approaches for modeling text as a living object in context. Our initiative covers three core areas:

InterText
Data and Benchmarking

Research-ready datasets and benchmarks for interconnected living texts.

Learn more
InterText
Cross-document NLP

Joint unified framework for modeling cross-document discourse.

Learn more
InterText
Applications

New generation of NLP applications for collaborative text work.

Learn more

News

Apr 2025

Three papers at NAACL. Intertextuality has many applications, and we are excited to share three InterText-related papers by our colleagues at UKP Lab, to appear at NAACL-2025! Jonathan Tonglet et al. address the problem of debunking misinformation in images. Max Glockner et al. investigate misrepresentation of scientific claims. And Tim Baumgärtner et al. explore question answering for academic peer reviews, winning an Outstanding Paper Award 🏆! Have a look at the preprints, and if you are at NAACL, visit their talks to learn more.

Apr 2025

Keynote at SIGIR-25. We are happy to announce that Iryna Gurevych will give a Keynote on the use of AI for science and expert-AI collaboration at SIGIR-2025. If you are at the conference, come to our talk to learn more about what InterText has been up to in the past months, and about other related initiatives at UKP Lab.

Apr 2025

🚀 NLPeer v.2 has arrived. After many months of hard work, we are happy to announce the release of the NLPeer v.2. corpus – a new iteration of the data collection initiative from ACL Rolling Review, ELIFE, PLOS and other venues. With over 1.8k papers, 1k reviews, 1k rebuttals and 480 meta-reviews, this is one of the largest, most complete and most diverse peer reviewing datasets to date. Learn more about the project here, or simply download the dataset and start experimenting!

Dec 2024

How do experts reason during peer review?... This question is crucial for successful expert-AI collaboration. In the new paper, we rethink peer review as a diagnostic reasoning process. We propose Natural Language Diagnostic Abductive Reasoning as a new family of text-based reasoning tasks, where experts analyze a text step by step to arrive at a verdict. Our unique dataset of over 4000k reasoning steps opens new frontiers in the study of expert-AI collaboration. Have a look at the preprint to learn more!

Oct 2024

Are LLMs good classifiers?... To find out, we propose a framework to study LLM fine-tuning for classification with generation- and encoding-based approaches. We apply it to the edit intent classification task and create Re3-Sci2.0: a new large-scale dataset of scientific document revisions with over 94k labeled edits. Have a look at the preprint, while we prepare the camera-ready for EMNLP!

Jul 2024

InterText at ACL-2024. Two InterText papers to appear at ACL-2024 in Bangkok! Qian Ruan will present our new dataset and approach for holistic modelling of document revision [1], and Furkan Şahinuç will talk about systematic exploration of creative multi-document NLG tasks in the age of LLMs [2]. While the authors are busy preparing their posters, take a look at the preprints and meet us at the conference!

Jul 2024

Introducing M2QA. Language and domain are two major sources of data variation in NLP, motivating the need for joint language-domain transfer. Yet, reliable evaluation remains a challenge. To address this gap, together with colleagues, we created M2QA - a new multi-domain multi-lingual QA benchmark that allows testing for domain and/or language transfer across 4 distinct languages and domains. Find out the details in our preprint, or get the data and start experimenting!

May 2024

New white paper on NLP for peer review. Peer review is at the core of modern science. Yet it is hard, time consuming and often unfair. What makes peer review challenging, how can NLP help, and where should it stand aside? A new, extensive white paper in collaboration with over 20 high-profile NLP and ML researchers lays the foundation for machine-assisted scientific quality control in the age of AI. The companion repository aggregates datasets for peer review assistance to help new researchers get started. Have a look and contribute!

Apr 2024

InterText at EACL-2024. Long documents are often structured, making it much easier for humans to navigate large texts. Is document structure encoded in long-document transformers, and how can their structure-awareness be improved? We investigate this with a novel probing suite and structure infusion kit in our new EACL paper.

Nov 2023

Related work from our colleagues. Peer review is one of the core objects of study in InterText. A closely related new work from our colleagues at UKP Lab and the University of Hamburg explores argumentation in peer reviews and rebuttals. Take a look at their pre-print and visit their talk at the upcoming EMNLP!

Team

Iryna Gurevych

Principal Investigator

Ilia Kuznetsov

Postdoc

Jan Buchmann

PhD Student

Nils Dycke

PhD Student

Qian Ruan

PhD Student

Dennis Zyska

PhD Student

Sheng Lu

PhD Student

Yiwei Wang

PhD Student

Serwar Basch

PhD Student

Funding