2025 EMNLP EMNLP 2025

Exploring Coreference Resolution in Glosses of German Sign Language

Abstract

AbstractIn recent years, research on sign languages has attracted increasing attention in the NLP community and requires more effort from a linguistic perspective. In this paper, we explore coreference resolution in German Sign Language (GSL) primarily through gloss-based analysis. Specifically, in GSL glosses, we conduct a linguistic analysis of coreference, add coreference annotations based on one video, and evaluate the ability of two large language models to resolve coreference. We gain valuable insights into coreference resolution in GSL, which pave the way for future research.

🧭 Keyword Pioneer — gloss analysis
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio