Evaluating the state-of-the-art of End-to-End Natural Language Generation: The E2E NLG challenge

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Evaluating the state-of-the-art of End-to-End Natural Language Generation: The E2E NLG challenge

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

Evaluating the state-of-the-art of End-to-End Natural Language Generation: The E2E NLG challenge

Popis výsledku anglicky

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

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Computer Speech and Language

ISSN

0885-2308

e-ISSN

—

Svazek periodika

59

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

34

Strana od-do

123-156

Kód UT WoS článku

000490540900008

EID výsledku v databázi Scopus

2-s2.0-85070102543