Weakly Supervised Domain Adaptation for Aspect Extraction via Multilevel Interaction Transfer

Fine-grained aspect term extraction is an essential subtask in aspect-based opinion analysis. It aims to identify the aspect terms (also known as opinion targets) of a product or service in each sentence. To learn a good aspect extraction model, an expensive annotation process is usually involved to acquire sufficient token-level labels for each domain, which is not realistic. To address this limitation, some previous works propose domain adaptation strategies to transfer knowledge from a sufficiently labeled source domain to unlabeled target domains. However, due to both the difficulty of fine-grained prediction problems and the large domain gap between different domains, the performance is still far from satisfactory. In this work, we conduct a pioneer study on leveraging sentence-level aspect category labels that can be usually available in commercial services, such as review sites or social media to promote token-level transfer for extraction purpose. Specifically, the aspect category information can be used to construct pivot knowledge for transfer with the assumption that the interactions between the sentence-level aspect category and the token-level aspect terms are invariant across domains. To this end, we propose a novel multilevel reconstruction mechanism that aligns both the fine- and coarse-grained information in multiple levels of abstractions. Comprehensive experiments over several benchmark data sets clearly demonstrate that our approach can fully utilize the sentence-level aspect category labels to improve cross-domain aspect term extraction with a large performance gain.