Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning

Kavli Affiliate: Li Xin Li

| First 5 Authors: Zheng Li, Xin Li, Ying Wei, Lidong Bing, Yu Zhang

| Summary:

Joint extraction of aspects and sentiments can be effectively formulated as a
sequence labeling problem. However, such formulation hinders the effectiveness
of supervised methods due to the lack of annotated sequence data in many
domains. To address this issue, we firstly explore an unsupervised domain
adaptation setting for this task. Prior work can only use common syntactic
relations between aspect and opinion words to bridge the domain gaps, which
highly relies on external linguistic resources. To resolve it, we propose a
novel Selective Adversarial Learning (SAL) method to align the inferred
correlation vectors that automatically capture their latent relations. The SAL
method can dynamically learn an alignment weight for each word such that more
important words can possess higher alignment weights to achieve fine-grained
(word-level) adaptation. Empirically, extensive experiments demonstrate the
effectiveness of the proposed SAL method.

| Search Query: ArXiv Query: search_query=au:”Li Xin Li”&id_list=&start=0&max_results=10

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