  Structured sentiment analysis, which aims to extract the complex semantic
structures such as holders, expressions, targets, and polarities, has obtained
widespread attention from both industry and academia. Unfortunately, the
existing structured sentiment analysis datasets refer to a few languages and
are relatively small, limiting neural network models' performance. In this
paper, we focus on the cross-lingual structured sentiment analysis task, which
aims to transfer the knowledge from the source language to the target one.
Notably, we propose a Knowledge-Enhanced Adversarial Model (\texttt{KEAM}) with
both implicit distributed and explicit structural knowledge to enhance the
cross-lingual transfer. First, we design an adversarial embedding adapter for
learning an informative and robust representation by capturing implicit
semantic information from diverse multi-lingual embeddings adaptively. Then, we
propose a syntax GCN encoder to transfer the explicit semantic information
(e.g., universal dependency tree) among multiple languages. We conduct
experiments on five datasets and compare \texttt{KEAM} with both the supervised
and unsupervised methods. The extensive experimental results show that our
\texttt{KEAM} model outperforms all the unsupervised baselines in various
metrics.
