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The state-of-the-art model for structured sentiment analysis casts the taskas a dependency parsing problem, which has some limitations: (1) The labelproportions for span prediction and span relation prediction are imbalanced.(2) The span lengths of sentiment tuple components may be very large in thistask, which will further exacerbate the imbalance problem. (3) Two nodes in adependency graph cannot have multiple arcs, therefore some overlapped sentimenttuples cannot be recognized. In this work, we propose nichetargeting solutionsfor these issues. First, we introduce a novel labeling strategy, which containstwo 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 arerelatively balanced and applied in the prediction layer. The whole label setincludes rich labels to help our model capture various token relations, whichare applied in the hidden layer to softly influence our model. Moreover, wealso propose an effective model to well collaborate with our labeling strategy,which is equipped with the graph attention networks to iteratively refine tokenrepresentations, and the adaptive multi-label classifier to dynamically predictmultiple relations between token pairs. We perform extensive experiments on 5benchmark datasets in four languages. Experimental results show that our modeloutperforms previous SOTA models by a large margin.

Effective Token Graph Modeling using a Novel Labeling Strategy for Structured Sentiment Analysis