Natural language generation from Universal Dependencies using data augmentation and pre-trained language models

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AEEYSAUH5" target="_blank" >RIV/00216208:11320/23:EEYSAUH5 - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147539360&doi=10.1504%2fIJIIDS.2023.10053426&partnerID=40&md5=536d01463061350f89f904914ea31353" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147539360&doi=10.1504%2fIJIIDS.2023.10053426&partnerID=40&md5=536d01463061350f89f904914ea31353</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1504/ijiids.2023.10053426" target="_blank" >10.1504/ijiids.2023.10053426</a>

Result language

angličtina

Original language name

Natural language generation from Universal Dependencies using data augmentation and pre-trained language models

Original language description

"Natural language generation (NLG) has focused on data-to-text tasks with different structured inputs in recent years. The generated text should contain given information, be grammatically correct, and meet other criteria. We propose in this research an approach that combines solid pre-trained language models with input data augmentation. The studied data in this work are Universal Dependencies (UDs) which is developed as a framework for consistent annotation of grammar (parts of speech, morphological features and syntactic dependencies) for cross-lingual learning. We study the English UD structures, which are modified into two groups. In the first group, the modification phase is to remove the order information of each word and lemmatise the tokens. In the second group, the modification phase is to remove the functional words and surface-oriented morphological details. With both groups of modified structures, we apply the same approach to explore how pre-trained sequence-to-sequence models text-to-text transfer transformer (T5) and BART perform on the training data. We augment the training data by creating several permutations for each input structure. The result shows that our approach can generate good quality English text with the exciting idea of studying strategies to represent UD inputs. Copyright © 2023 Inderscience Enterprises Ltd."

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS 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)

Project

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Continuities

—

Publication year

2023

Confidentiality

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

Name of the periodical

"International Journal of Intelligent Information and Database Systems"

ISSN

1751-5858

e-ISSN

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Volume of the periodical

16

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

17

Pages from-to

89-105

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85147539360