  Aspect-level sentiment classification (ALSC) aims at identifying the
sentiment polarity of a specified aspect in a sentence. ALSC is a practical
setting in aspect-based sentiment analysis due to no opinion term labeling
needed, but it fails to interpret why a sentiment polarity is derived for the
aspect. To address this problem, recent works fine-tune pre-trained Transformer
encoders for ALSC to extract an aspect-centric dependency tree that can locate
the opinion words. However, the induced opinion words only provide an intuitive
cue far below human-level interpretability. Besides, the pre-trained encoder
tends to internalize an aspect's intrinsic sentiment, causing sentiment bias
and thus affecting model performance. In this paper, we propose a span-based
anti-bias aspect representation learning framework. It first eliminates the
sentiment bias in the aspect embedding by adversarial learning against aspects'
prior sentiment. Then, it aligns the distilled opinion candidates with the
aspect by span-based dependency modeling to highlight the interpretable opinion
terms. Our method achieves new state-of-the-art performance on five benchmarks,
with the capability of unsupervised opinion extraction.
