Work

The E2E Dataset: New Challenges For End-to-End Generation

Year: 2017

Type: conference-paper

Abstract: This paper describes the E2E data, a new dataset for training end-to-end, datadriven 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.

Source: Annual SIGdial Meeting on Discourse and Dialogue (SIGDIAL)

DOI: 10.18653/v1/w17-5525

Authors: Jekaterina Novikova, Ondřej Dušek, Verena Rieser

Institution: Heriot-Watt University

Language: English

Topic: Speech and dialogue systems

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Nvidia, Engineering and Physical Sciences Research Council

Awards: MaDrIgAL: MultiDimensional Interaction management and Adaptive Learning, G5709710992, G6629039517, DILiGENt: Domain-Independent Language Generation