Improving Semantic Parsing and Text Generation through Multi-Faceted Data Augmentation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AB2PYX49B" target="_blank" >RIV/00216208:11320/26:B2PYX49B - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ACCESS.2025.3593857" target="_blank" >http://dx.doi.org/10.1109/ACCESS.2025.3593857</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2025.3593857" target="_blank" >10.1109/ACCESS.2025.3593857</a>
Alternative languages
Result language
angličtina
Original language name
Improving Semantic Parsing and Text Generation through Multi-Faceted Data Augmentation
Original language description
The increasing use of large language models has heightened the demand for more extensive datasets in natural language processing (NLP). While various augmentation techniques are being employed to enhance data quantity, many introduce noise or struggle with structurally complex inputs like Discourse Representation Structures (DRS). This study introduces novel data augmentation techniques for both semantic parsing (Text-to-DRS) and text generation (DRS-to-Text), emphasizing enhancements such as named entity augmentation, lexical substitutions utilizing WordNet, and grammatical transformations through changes in tense. The proposed methods led to a considerable expansion of the Parallel Meaning Bank (PMB) dataset, ensuring semantic accuracy and contextual relevance. The augmentation increased both gold and silver instances by a factor of 9, resulting in over 1.3 million new examples. We evaluated four transformer models (byT5, mT5, T5, and mBART) using this augmented dataset. Experimental evaluations revealed substantial improvements across multiple performance metrics. Notably, for semantic parsing, we observed a 17.65% increase in SMATCH (F1) score, and among different evaluation measures for text generation, we have improvements of 14.38% in BLEU score and 6.43% in METEOR score. The observed improvements highlight the effectiveness of our proposed augmentation methodologies in boosting model capabilities for complex neural semantic parsing and generation tasks. © IEEE. 2013 IEEE.
Czech name
—
Czech description
—
Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
—
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Access
ISSN
2169-3536
e-ISSN
—
Volume of the periodical
2025
Issue of the periodical within the volume
2025
Country of publishing house
US - UNITED STATES
Number of pages
23
Pages from-to
150145-150167
UT code for WoS article
—
EID of the result in the Scopus database
2-s2.0-105012296463