LACA: Improving Cross-lingual Aspect-Based Sentiment Analysis with LLM Data Augmentation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F25%3A43976214" target="_blank" >RIV/49777513:23520/25:43976214 - isvavai.cz</a>

Result on the web

<a href="https://aclanthology.org/2025.acl-long.41/" target="_blank" >https://aclanthology.org/2025.acl-long.41/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18653/v1/2025.acl-long.41" target="_blank" >10.18653/v1/2025.acl-long.41</a>

Result language

angličtina

Original language name

LACA: Improving Cross-lingual Aspect-Based Sentiment Analysis with LLM Data Augmentation

Original language description

Cross-lingual aspect-based sentiment analysis (ABSA) involves detailed sentiment analysis in a target language by transferring knowledge from a source language with available annotated data. Most existing methods depend heavily on often unreliable translation tools to bridge the language gap. In this paper, we propose a new approach that leverages a large language model (LLM) to generate high-quality pseudo-labelled data in the target language without the need for translation tools. First, the framework trains an ABSA model to obtain predictions for unlabelled target language data. Next, LLM is prompted to generate natural sentences that better represent these noisy predictions than the original text. The ABSA model is then further fine-tuned on the resulting pseudo-labelled dataset. We demonstrate the effectiveness of this method across six languages and five backbone models, surpassing previous state-of-the-art translation-based approaches. The proposed framework also supports generative models, and we show that fine-tuned LLMs outperform smaller multilingual models.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EH23_021%2F0008436" target="_blank" >EH23_021/0008436: RandD of technologies for advanced digitization in the Pilsen metropolitan area (DigiTech)</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

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

Article name in the collection

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

ISBN

979-8-89176-251-0

ISSN

—

e-ISSN

—

Number of pages

15

Pages from-to

839-853

Publisher name

Association for Computational Linguistics

Place of publication

Kerrville

Event location

Vídeň

Event date

Jul 27, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001596029800041