  The state-of-the-art model for structured sentiment analysis casts the task
as a dependency parsing problem, which has some limitations: (1) The label
proportions for span prediction and span relation prediction are imbalanced.
(2) The span lengths of sentiment tuple components may be very large in this
task, which will further exacerbate the imbalance problem. (3) Two nodes in a
dependency graph cannot have multiple arcs, therefore some overlapped sentiment
tuples cannot be recognized. In this work, we propose nichetargeting solutions
for these issues. First, we introduce a novel labeling strategy, which contains
two sets of token pair labels, namely essential label set and whole label set.
The essential label set consists of the basic labels for this task, which are
relatively balanced and applied in the prediction layer. The whole label set
includes rich labels to help our model capture various token relations, which
are applied in the hidden layer to softly influence our model. Moreover, we
also propose an effective model to well collaborate with our labeling strategy,
which is equipped with the graph attention networks to iteratively refine token
representations, and the adaptive multi-label classifier to dynamically predict
multiple relations between token pairs. We perform extensive experiments on 5
benchmark datasets in four languages. Experimental results show that our model
outperforms previous SOTA models by a large margin.
