  Fine-grained aspect extraction is an essential sub-task in aspect based
opinion analysis. It aims to identify the aspect terms (a.k.a. opinion targets)
of a product or service in each sentence. However, expensive annotation process
is usually involved to acquire sufficient token-level labels for each domain.
To address this limitation, some previous works propose domain adaptation
strategies to transfer knowledge from a sufficiently labeled source domain to
unlabeled target domains. But due to both the difficulty of fine-grained
prediction problems and the large domain gap between domains, the performance
remains unsatisfactory. This work conducts a pioneer study on leveraging
sentence-level aspect category labels that can be usually available in
commercial services like review sites to promote token-level transfer for the
extraction purpose. Specifically, the aspect category information is used to
construct pivot knowledge for transfer with assumption that the interactions
between sentence-level aspect category and token-level aspect terms are
invariant across domains. To this end, we propose a novel multi-level
reconstruction mechanism that aligns both the fine-grained and coarse-grained
information in multiple levels of abstractions. Comprehensive experiments
demonstrate that our approach can fully utilize sentence-level aspect category
labels to improve cross-domain aspect extraction with a large performance gain.
