The E2E Dataset: New Challenges For End-to-End Generation
This provides a more challenging benchmark for researchers in natural language generation, though it is incremental as it builds on existing datasets.
The paper introduces the E2E dataset, a new and larger dataset for end-to-end natural language generation in the restaurant domain, which presents challenges like increased lexical richness and content selection, and establishes a baseline to illustrate these difficulties.
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.