Joint Generator-Ranker Learning for Natural Language Generation. (arXiv:2206.13974v1 [cs.CL])

Due to exposure bias, most existing natural language generation (NLG) models
trained by maximizing the likelihood objective predict poor text results during
the inference stage. In this paper, to tackle this problem, we revisit the
generate-then-rank framework and propose a joint generator-ranker (JGR)
training algorithm for text generation tasks. In JGR, the generator model is
trained by maximizing two objectives: the likelihood of the training corpus and
the expected reward given by the ranker model. Meanwhile, the ranker model
takes …

arxiv generation generator language language generation learning natural natural language natural language generation