@inproceedings{dusek-etal-2018-findings,
title = "Findings of the {E}2{E} {NLG} Challenge",
author = "Du\v sek, Ond\v rej and
Novikova, Jekaterina and
Rieser, Verena",
editor = "Krahmer, Emiel and
Gatt, Albert and
Goudbeek, Martijn",
booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
month = nov,
year = "2018",
address = "Tilburg University, The Netherlands",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6539/",
doi = "10.18653/v1/W18-6539",
pages = "322--328",
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."
}
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%0 Conference Proceedings
%T Findings of the E2E NLG Challenge
%A Dušek, Ond\v rej
%A Novikova, Jekaterina
%A Rieser, Verena
%Y Krahmer, Emiel
%Y Gatt, Albert
%Y Goudbeek, Martijn
%S Proceedings of the 11th International Conference on Natural Language Generation
%D 2018
%8 November
%I Association for Computational Linguistics
%C Tilburg University, The Netherlands
%F dusek-etal-2018-findings
%X 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.
%R 10.18653/v1/W18-6539
%U https://aclanthology.org/W18-6539/
%U https://doi.org/10.18653/v1/W18-6539
%P 322-328
Markdown (Informal)
[Findings of the E2E NLG Challenge](https://aclanthology.org/W18-6539/) (Dušek et al., INLG 2018)
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.