This paper describes the E2E data, a new dataset for training end-to-end, data-driven natural language generation systems in the restaurant domain, which is ten times bigger than existing, frequently used datasets in this area. The E2E dataset poses new challenges: (1) its human reference texts show more lexical richness and syntactic variation, including discourse phenomena; (2) generating from this set requires content selection. As such, learning from this dataset promises more natural, varied and less template-like system utterances. We also establish a baseline on this dataset, which illustrates some of the difficulties associated with this data.
@article{arxiv.1706.09254,
title = {The E2E Dataset: New Challenges For End-to-End Generation},
author = {Jekaterina Novikova and Ondřej Dušek and Verena Rieser},
journal= {arXiv preprint arXiv:1706.09254},
year = {2017}
}
Comments
Accepted as a short paper for SIGDIAL 2017 (final submission including supplementary material)