61 - Neural Text Generation in Stories, with Elizabeth Clark and Yangfeng Ji

NAACL 2018 Outstanding Paper by Elizabeth Clark, Yangfeng Ji, and
Noah A. Smith Both Elizabeth and Yangfeng come on the podcast to
tell us about their work. This paper is an extension of an EMNLP
2017 paper by Yangfeng and co-authors that introduced a language
model that included explicit entity representations. Elizabeth and
Yangfeng take that model, improve it a bit, and use it for creative
narrative generation, with some interesting applications. We talk a
little bit about the model, but mostly about how the model was used
to generate narrative text, how it was evaluated, and what other
interesting applications there are of this idea. The punchline is
that this model does a better job at generating coherent stories
than other generation techniques, because it can track the entities
in the story better. We've been experimenting with how we record
the audio, trying to figure out how to get better audio quality.
Sadly, this episode was a failed experiment, and there is a
background hiss that we couldn't get rid of. Bear with us as we
work on this...
https://www.semanticscholar.org/paper/Generation-in-Stories-Using-Entity-Representations-Clark-Ji/56df50601975f6065b8acc0a08c169aaecad97bc