Findings of the E2E NLG Challenge

Ondřej Dušek, Jekaterina Novikova, Verena Rieser


Abstract
This paper summarises the experimental setup and results of the first shared task on end-to-end (E2E) natural language generation (NLG) in spoken dialogue systems. Recent end-to-end generation systems are promising since they reduce the need for data annotation. However, they are currently limited to small, delexicalised datasets. The E2E NLG shared task aims to assess whether these novel approaches can generate better-quality output by learning from a dataset containing higher lexical richness, syntactic complexity and diverse discourse phenomena. We compare 62 systems submitted by 17 institutions, covering a wide range of approaches, including machine learning architectures – with the majority implementing sequence-to-sequence models (seq2seq) – as well as systems based on grammatical rules and templates.
Anthology ID:
W18-6539
Volume:
Proceedings of the 11th International Conference on Natural Language Generation
Month:
November
Year:
2018
Address:
Tilburg University, The Netherlands
Editors:
Emiel Krahmer, Albert Gatt, Martijn Goudbeek
Venue:
INLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
322–328
Language:
URL:
https://aclanthology.org/W18-6539
DOI:
10.18653/v1/W18-6539
Bibkey:
Cite (ACL):
Ondřej Dušek, Jekaterina Novikova, and Verena Rieser. 2018. Findings of the E2E NLG Challenge. In Proceedings of the 11th International Conference on Natural Language Generation, pages 322–328, Tilburg University, The Netherlands. Association for Computational Linguistics.
Cite (Informal):
Findings of the E2E NLG Challenge (Dušek et al., INLG 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-5/W18-6539.pdf
Data
E2ERoboCup