Mod-D2T: A Multi-layer Dataset for Modular Data-to-Text Generation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AK35LBLCV" target="_blank" >RIV/00216208:11320/23:K35LBLCV - isvavai.cz</a>

Výsledek na webu

<a href="https://aclanthology.org/2023.inlg-main.36/" target="_blank" >https://aclanthology.org/2023.inlg-main.36/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18653/v1/2023.inlg-main.36" target="_blank" >10.18653/v1/2023.inlg-main.36</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Mod-D2T: A Multi-layer Dataset for Modular Data-to-Text Generation

Popis výsledku v původním jazyce

"Rule-based text generators lack the coverage and fluency of their neural counterparts, but have two big advantages over them: (i) they are entirely controllable and do not hallucinate; and (ii) they can fully explain how an output was generated from an input. In this paper we leverage these two advantages to create large and reliable synthetic datasets with multiple human-intelligible intermediate representations. We present the Modular Data-to-Text (Mod-D2T) Dataset which incorporates ten intermediate-level representations between input triple sets and output text; the mappings from one level to the next can broadly be interpreted as the traditional modular tasks of an NLG pipeline. We describe the Mod-D2T dataset, evaluate its quality via manual validation and discuss its applications and limitations. Data, code and documentation are available at https://github.com/mille-s/Mod-D2T."

Název v anglickém jazyce

Mod-D2T: A Multi-layer Dataset for Modular Data-to-Text Generation

Popis výsledku anglicky

"Rule-based text generators lack the coverage and fluency of their neural counterparts, but have two big advantages over them: (i) they are entirely controllable and do not hallucinate; and (ii) they can fully explain how an output was generated from an input. In this paper we leverage these two advantages to create large and reliable synthetic datasets with multiple human-intelligible intermediate representations. We present the Modular Data-to-Text (Mod-D2T) Dataset which incorporates ten intermediate-level representations between input triple sets and output text; the mappings from one level to the next can broadly be interpreted as the traditional modular tasks of an NLG pipeline. We describe the Mod-D2T dataset, evaluate its quality via manual validation and discuss its applications and limitations. Data, code and documentation are available at https://github.com/mille-s/Mod-D2T."

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

—

Návaznosti

—

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

"Proceedings of the 16th International Natural Language Generation Conference"

ISBN

979-8-89176-001-1

ISSN

—

e-ISSN

—

Počet stran výsledku

12

Strana od-do

455-466

Název nakladatele

""

Místo vydání

Prague, Czechia

Místo konání akce

Prague, Czechia

Datum konání akce

1. 1. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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