Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction
<|reference_start|>Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction: 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 release our code and data at https://github.com/HITSZ-HLT/ST-w-Scorer-ABSA .<|reference_end|>
@article{zhang2024self-training,
    title={Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad
  Prediction},
    author={Yice Zhang, Jie Zeng, Weiming Hu, Ziyi Wang, Shiwei Chen, Ruifeng Xu},
    journal={arXiv preprint arXiv:2406.18078},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18078},
    primaryClass={cs.CL cs.AI}
}