“Do Massively Pretrained Language Models Make Better Storytellers?”, 2019-09-24 (; backlinks; similar):
Large neural language models trained on massive amounts of text have emerged as a formidable strategy for Natural Language Understanding tasks. However, the strength of these models as Natural Language Generators is less clear. Though anecdotal evidence suggests that these models generate better quality text, there has been no detailed study characterizing detailed study characterizing their generation abilities.
In this work, we compare the performance of an extensively pretrained model, OpenAI GPT-2-117m ( et al 2019), to a state-of-the-art neural story generation model ( et al 2018). By evaluating the generated text across a wide variety of automatic metrics, we characterize the ways in which pretrained models do, and do not, make better storytellers.
We find that although GPT-2-117m conditions more strongly on context, is more sensitive to ordering of events, and uses more unusual words, it is just as likely to produce repetitive and under-diverse text when using likelihood-maximizing decoding algorithms.