March 27, 2024, 4:48 a.m. | Yichen Huang, Ekaterina Kochmar

cs.CL updates on arXiv.org arxiv.org

arXiv:2403.17640v1 Announce Type: new
Abstract: Text simplification lacks a universal standard of quality, and annotated reference simplifications are scarce and costly. We propose to alleviate such limitations by introducing REFeREE, a reference-free model-based metric with a 3-stage curriculum. REFeREE leverages an arbitrarily scalable pretraining stage and can be applied to any quality standard as long as a small number of human annotations are available. Our experiments show that our metric outperforms existing reference-based metrics in predicting overall ratings and reaches …

abstract arxiv cs.cl curriculum free limitations pretraining quality reference scalable stage standard text type universal

Founding AI Engineer, Agents

@ Occam AI | New York

AI Engineer Intern, Agents

@ Occam AI | US

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Business Intelligence Architect - Specialist

@ Eastman | Hyderabad, IN, 500 008