Improving Semantic Parsing and Text Generation through Multi-Faceted Data Augmentation

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Improving Semantic Parsing and Text Generation through Multi-Faceted Data Augmentation

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Improving Semantic Parsing and Text Generation through Multi-Faceted Data Augmentation

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

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

2025

Kód důvěrnosti údajů

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

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

—

Svazek periodika

2025

Číslo periodika v rámci svazku

2025

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

23

Strana od-do

150145-150167

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-105012296463