  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 .
