  Aspect-based sentiment classification (ASC) is an important task in
fine-grained sentiment analysis.~Deep supervised ASC approaches typically model
this task as a pair-wise classification task that takes an aspect and a
sentence containing the aspect and outputs the polarity of the aspect in that
sentence. However, we discovered that many existing approaches fail to learn an
effective ASC classifier but more like a sentence-level sentiment classifier
because they have difficulty to handle sentences with different polarities for
different aspects.~This paper first demonstrates this problem using several
state-of-the-art ASC models. It then proposes a novel and general adaptive
re-weighting (ARW) scheme to adjust the training to dramatically improve ASC
for such complex sentences. Experimental results show that the proposed
framework is effective \footnote{The dataset and code are available at
\url{https://github.com/howardhsu/ASC_failure}.}.
