Towards Neural Language Evaluators
arXiv:1909.09268
Abstract
We review three limitations of BLEU and ROUGE -- the most popular metrics used to assess reference summaries against hypothesis summaries, come up with criteria for what a good metric should behave like and propose concrete ways to use recent Transformers-based Language Models to assess reference summaries against hypothesis summaries.
Accepted to NeurIPS 2019 Document Intelligence Workshop