short-paper

A Simple yet Effective Framework for Few-Shot Aspect-Based Sentiment Analysis

Authors:

Rui XiaAuthors Info & Claims

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1765 - 1770

https://doi.org/10.1145/3539618.3591940

Published: 18 July 2023 Publication History

Abstract

The pre-training and fine-tuning paradigm has become the main-stream framework in the field of Aspect-Based Sentiment Analysis (ABSA). Although it has achieved sound performance in the domains containing enough fine-grained aspect-sentiment annotations, it is still challenging to conduct few-shot ABSA in domains where manual annotations are scarce. In this work, we argue that two kinds of gaps, i.e., domain gap and objective gap, hinder the transfer of knowledge from pre-training language models (PLMs) to ABSA tasks. To address this issue, we introduce a simple yet effective framework called FS-ABSA, which involves domain-adaptive pre-training and text-infilling fine-tuning. We approach the End-to-End ABSA task as a text-infilling problem and perform domain-adaptive pre-training with the text-infilling objective, narrowing the two gaps and consequently facilitating the knowledge transfer. Experiments show that the resulting model achieves more compelling performance than baselines under the few-shot setting while driving the state-of-the-art performance to a new level across datasets under the fully-supervised setting. Moreover, we apply our framework to two non-English low-resource languages to demonstrate its generality and effectiveness.

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Cited By

Lu HLiu TCong RYang JGan QFang WWu X(2025)QAIEInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10391762:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.ipm.2024.103917
Wang QDing KLuo XXu RHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Improving In-Context Learning via Sequentially Selection and Preference Alignment for Few-Shot Aspect-Based Sentiment AnalysisProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657932(2462-2466)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657932
Hemmatizadeh FWong CYu AFani HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)LADy 💃: A Benchmark Toolkit for Latent Aspect Detection Enriched with Backtranslation AugmentationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657894(1172-1178)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657894

Index Terms

A Simple yet Effective Framework for Few-Shot Aspect-Based Sentiment Analysis
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Sentiment analysis

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cover image ACM Conferences

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2023

3567 pages

ISBN:9781450394086

DOI:10.1145/3539618

General Chairs:
Hsin-Hsi Chen
National Taiwan University
,
Wei-Jou (Edward) Duh
National Taiwan University
,
Hen-Hsen Huang
Academia Sinica
,
Program Chairs:
Makoto P. Kato
Spotify
,
Josiane Mothe
Universite de Toulouse
,
Barbara Poblete
University of Chile and Amazon Visiting Academic

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Published: 18 July 2023

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the Natural Science Foundation of Jiangsu Province for Distinguished Young Scholars
the Natural Science Foundation of China

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SIGIR '23

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SIGIR

SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 23 - 27, 2023

Taipei, Taiwan

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Lu HLiu TCong RYang JGan QFang WWu X(2025)QAIEInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10391762:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.ipm.2024.103917
Wang QDing KLuo XXu RHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Improving In-Context Learning via Sequentially Selection and Preference Alignment for Few-Shot Aspect-Based Sentiment AnalysisProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657932(2462-2466)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657932
Hemmatizadeh FWong CYu AFani HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)LADy 💃: A Benchmark Toolkit for Latent Aspect Detection Enriched with Backtranslation AugmentationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657894(1172-1178)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657894

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