108 - Data-To-Text Generation, with Verena Rieser and Ondřej Dušek

In this episode we invite Verena Rieser and Ondřej Dušek on to
talk to us about the complexities of generating natural language
when you have some kind of structured meaning representation as
input. We talk about when you might want to do this, which is often
is some kind of a dialog system, but also generating game
summaries, and even some language modeling work. We then talk about
why this is hard, which in large part is due to the difficulty of
collecting data, and how to evaluate the output of these systems.
We then move on to discussing the details of a major challenge that
Verena and Ondřej put on, called the end-to-end natural language
generation challenge (E2E NLG). This was a dataset of task-based
dialog generation focused on the restaurant domain, with some very
innovative data collection techniques. They held a shared task with
16 participating teams in 2017, and the data has been further used
since. We talk about the methods that people used for the task, and
what we can learn today from what methods have been used on this
data. Verena's website:
https://sites.google.com/site/verenateresarieser/ Ondřej's
website: https://tuetschek.github.io/ The E2E NLG Challenge that we
talked about quite a bit:
http://www.macs.hw.ac.uk/InteractionLab/E2E/