Exploiting Task Reversibility of DRS Parsing and Generation: Challenges and Insights from Multilingual Perspective

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3ADYU6CFK7" target="_blank" >RIV/00216208:11320/26:DYU6CFK7 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Exploiting Task Reversibility of DRS Parsing and Generation: Challenges and Insights from Multilingual Perspective

Popis výsledku v původním jazyce

Semantic parsing and text generation exhibit reversible properties when utilizing Discourse Representation Structures (DRS). However, both processes—text-to-DRS parsing and DRS-to-text generation—are susceptible to errors. In this paper, we exploit the reversible nature of DRS to explore both error propagation, which is commonly seen in pipeline methods, and the less frequently studied potential for error correction. We investigate two pipeline approaches: Parse-Generate-Parse (PGP) and Generate-Parse-Generate (GPG), utilizing pre-trained language models where the output of one model becomes the input for the next. Our evaluation uses the Parallel Meaning Bank dataset, focusing on Urdu as a low-resource language, Italian as a mid-resource language, and English serving as a high-resource baseline. Our analysis highlights that, while pipelines are theoretically suited for error correction, they more often propagate errors, with Urdu exhibiting the greatest sensitivity, Italian showing a moderate effect, and English demonstrating the highest stability. This variation highlights the unique challenges faced by low-resource languages in semantic processing tasks. Further, our findings suggest that these pipeline methods support the development of more linguistically balanced datasets, enabling a comprehensive assessment across factors like sentence structure, length, type, polarity, and voice. Our cross-linguistic analysis provides valuable insights into the behavior of DRS processing in low-resource contexts, demonstrating both the potential and limitations of reversible pipeline approaches. © 2025 Association for Computational Linguistics.

Název v anglickém jazyce

Exploiting Task Reversibility of DRS Parsing and Generation: Challenges and Insights from Multilingual Perspective

Popis výsledku anglicky

Semantic parsing and text generation exhibit reversible properties when utilizing Discourse Representation Structures (DRS). However, both processes—text-to-DRS parsing and DRS-to-text generation—are susceptible to errors. In this paper, we exploit the reversible nature of DRS to explore both error propagation, which is commonly seen in pipeline methods, and the less frequently studied potential for error correction. We investigate two pipeline approaches: Parse-Generate-Parse (PGP) and Generate-Parse-Generate (GPG), utilizing pre-trained language models where the output of one model becomes the input for the next. Our evaluation uses the Parallel Meaning Bank dataset, focusing on Urdu as a low-resource language, Italian as a mid-resource language, and English serving as a high-resource baseline. Our analysis highlights that, while pipelines are theoretically suited for error correction, they more often propagate errors, with Urdu exhibiting the greatest sensitivity, Italian showing a moderate effect, and English demonstrating the highest stability. This variation highlights the unique challenges faced by low-resource languages in semantic processing tasks. Further, our findings suggest that these pipeline methods support the development of more linguistically balanced datasets, enabling a comprehensive assessment across factors like sentence structure, length, type, polarity, and voice. Our cross-linguistic analysis provides valuable insights into the behavior of DRS processing in low-resource contexts, demonstrating both the potential and limitations of reversible pipeline approaches. © 2025 Association for Computational Linguistics.

Druh

D - Stať ve sborníku

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

—

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proc. Main Conf. Int. Conf. Comput. Linguist., COLING

ISBN

979-8-89176-215-2

ISSN

—

e-ISSN

—

Počet stran výsledku

19

Strana od-do

268-286

Název nakladatele

Association for Computational Linguistics (ACL)

Místo vydání

—

Místo konání akce

Abu Dhabi

Datum konání akce

1. 1. 2026

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—