2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.LG2023
DSG: An End-to-End Document Structure Generator
Johannes Rausch, Gentiana Rashiti, Maxim Gusev +2
Information in industry, research, and the public sector is widely stored as rendered documents (e.g., PDF files, scans). Hence, to enable downstream tasks, systems are needed that…
cs.CL2023★ 2 cited
Document-Level In-Context Few-Shot Relation Extraction via Pre-Trained Language Models
Yilmazcan Ozyurt, Stefan Feuerriegel, Ce Zhang
Document-level relation extraction aims at inferring structured human knowledge from textual documents. State-of-the-art methods for this task use pre-trained language models (LMs)…