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    <abstract>Aspect Sentiment Quad Prediction (ASQP) aims to predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review, which is the most representative and challenging task in aspect-based sentiment analysis. A key challenge in the ASQP task is the scarcity of labeled data, which limits the performance of existing methods. To tackle this issue, we propose a self-training framework with a pseudo-label scorer, wherein a scorer assesses the match between reviews and their pseudo-labels, aiming to filter out mismatches and thereby enhance the effectiveness of self-training. We highlight two critical aspects to ensure the scorer’s effectiveness and reliability: the quality of the training dataset and its model architecture. To this end, we create a human-annotated comparison dataset and train a generative model on it using ranking-based objectives. Extensive experiments on public ASQP datasets reveal that using our scorer can greatly and consistently improve the effectiveness of self-training. Moreover, we explore the possibility of replacing humans with large language models for comparison dataset annotation, and experiments demonstrate its feasibility. We will release our code and data via GitHub.</abstract>
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%0 Conference Proceedings
%T Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction
%A Zhang, Yice
%A Zeng, Jie
%A Hu, Weiming
%A Wang, Ziyi
%A Chen, Shiwei
%A Xu, Ruifeng
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F zhang-etal-2024-self-training
%X Aspect Sentiment Quad Prediction (ASQP) aims to predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review, which is the most representative and challenging task in aspect-based sentiment analysis. A key challenge in the ASQP task is the scarcity of labeled data, which limits the performance of existing methods. To tackle this issue, we propose a self-training framework with a pseudo-label scorer, wherein a scorer assesses the match between reviews and their pseudo-labels, aiming to filter out mismatches and thereby enhance the effectiveness of self-training. We highlight two critical aspects to ensure the scorer’s effectiveness and reliability: the quality of the training dataset and its model architecture. To this end, we create a human-annotated comparison dataset and train a generative model on it using ranking-based objectives. Extensive experiments on public ASQP datasets reveal that using our scorer can greatly and consistently improve the effectiveness of self-training. Moreover, we explore the possibility of replacing humans with large language models for comparison dataset annotation, and experiments demonstrate its feasibility. We will release our code and data via GitHub.
%U https://aclanthology.org/2024.acl-long.640
%P 11862-11875

Markdown (Informal)

[Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction](https://aclanthology.org/2024.acl-long.640) (Zhang et al., ACL 2024)

• Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction (Zhang et al., ACL 2024)

ACL

• Yice Zhang, Jie Zeng, Weiming Hu, Ziyi Wang, Shiwei Chen, and Ruifeng Xu. 2024. Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11862–11875, Bangkok, Thailand. Association for Computational Linguistics.