Evaluating the state-of-the-art of End-to-End Natural Language Generation: The E2E NLG challenge
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10424317" target="_blank" >RIV/00216208:11320/20:10424317 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=U.k_HzDbrw" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=U.k_HzDbrw</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.csl.2019.06.009" target="_blank" >10.1016/j.csl.2019.06.009</a>
Alternative languages
Result language
angličtina
Original language name
Evaluating the state-of-the-art of End-to-End Natural Language Generation: The E2E NLG challenge
Original language description
This paper provides a comprehensive analysis of the first shared task on End-to-End Natural Language Generation (NLG) and identifies avenues for future research based on the results. This shared task aimed to assess whether recent end-to-end NLG systems can generate more complex output by learning from datasets containing higher lexical richness, syntactic complexity and diverse discourse phenomena. Introducing novel automatic and human metrics, 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. Seq2seq-based systems have demonstrated a great potential for NLG in the challenge. We find that seq2seq systems generally score high in terms of word-overlap metrics and human evaluations of naturalness - with the winning Slug system (Juraska et al., 2018) being seq2seq-based. However, vanilla seq2se
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Computer Speech and Language
ISSN
0885-2308
e-ISSN
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Volume of the periodical
59
Issue of the periodical within the volume
1
Country of publishing house
GB - UNITED KINGDOM
Number of pages
34
Pages from-to
123-156
UT code for WoS article
000490540900008
EID of the result in the Scopus database
2-s2.0-85070102543