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
this https URL .

Aspect Sentiment Quad Prediction (ASQP) 任务是在基于方面的情感分析中预测给定评论的所有四元组（方面术语、方面类别、意见术语、情感极性）的最具代表性和具有挑战性的任务。我们提出了一个自训练框架，其中包含一个伪标签评分器，用于评估评论与其伪标签之间的匹配程度，目的是过滤掉不匹配的部分，从而增强自训练的效果。我们还探索了使用大型语言模型替代人类进行对比数据集注释的可能性，并且实验证明了其可行性。