  Aspect-level sentiment classification (ASC) aims at identifying sentiment
polarities towards aspects in a sentence, where the aspect can behave as a
general Aspect Category (AC) or a specific Aspect Term (AT). However, due to
the especially expensive and labor-intensive labeling, existing public corpora
in AT-level are all relatively small. Meanwhile, most of the previous methods
rely on complicated structures with given scarce data, which largely limits the
efficacy of the neural models. In this paper, we exploit a new direction named
coarse-to-fine task transfer, which aims to leverage knowledge learned from a
rich-resource source domain of the coarse-grained AC task, which is more easily
accessible, to improve the learning in a low-resource target domain of the
fine-grained AT task. To resolve both the aspect granularity inconsistency and
feature mismatch between domains, we propose a Multi-Granularity Alignment
Network (MGAN). In MGAN, a novel Coarse2Fine attention guided by an auxiliary
task can help the AC task modeling at the same fine-grained level with the AT
task. To alleviate the feature false alignment, a contrastive feature alignment
method is adopted to align aspect-specific feature representations
semantically. In addition, a large-scale multi-domain dataset for the AC task
is provided. Empirically, extensive experiments demonstrate the effectiveness
of the MGAN.
