Mitigating Data Scarcity in Semantic Parsing across Languages: the Multilingual Semantic Layer and its Dataset

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AAR3G7CRK" target="_blank" >RIV/00216208:11320/25:AR3G7CRK - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205312109&partnerID=40&md5=f05eb79ade11970aac0d746ab2512c19" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205312109&partnerID=40&md5=f05eb79ade11970aac0d746ab2512c19</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Mitigating Data Scarcity in Semantic Parsing across Languages: the Multilingual Semantic Layer and its Dataset

Original language description

Data scarcity is a prevalent challenge in the era of Large Language Models (LLMs). The insatiable hunger of LLMs for large corpora becomes even more pronounced when dealing with non-English and low-resource languages. The issue is particularly exacerbated in Semantic Parsing (SP), i.e. the task of converting text into a formal representation. The complexity of semantic formalisms makes training human annotators and subsequent data annotation unfeasible on a large scale, especially across languages. To mitigate this, we first introduce the Multilingual Semantic Layer (MSL), a conceptual evolution of previous formalisms, which decouples from disambiguation and external inventories and simplifies the task. MSL provides the necessary tools to encode the meaning across languages, paving the way for developing a high-quality semantic parsing dataset across different languages in a semi-automatic strategy. Subsequently, we manually refine a portion of this dataset and fine-tune GPT-3.5 to propagate these refinements across the dataset. Then, we manually annotate 1,100 sentences in eleven languages, including low-resource ones. Finally, we assess our dataset's quality, showcasing the performance gap reduction across languages in Semantic Parsing. Our code and dataset are openly available at https://github.com/SapienzaNLP/MSL. © 2024 Association for Computational Linguistics.

Czech name

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Czech description

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Type

D - Article in proceedings

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

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Publication year

2024

Confidentiality

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

Article name in the collection

Proc. Annu. Meet. Assoc. Comput Linguist.

ISBN

979-889176099-8

ISSN

0736-587X

e-ISSN

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Number of pages

25

Pages from-to

14056-14080

Publisher name

Association for Computational Linguistics (ACL)

Place of publication

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Event location

Bangkok

Event date

Jan 1, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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