Attention scorers have achieved success in parsing tasks like semantic and syntactic dependency parsing. However, in tasks modeled into parsing, like structured sentiment analysis, "dependency edges" are very sparse which hinders parser performance. Thus we propose a sparse and fuzzy attention scorer with pooling layers which improves parser performance and sets the new state-of-the-art on structured sentiment analysis. We further explore the parsing modeling on structured sentiment analysis with second-order parsing and introduce a novel sparse second-order edge building procedure that leads to significant improvement in parsing performance.
翻译：关注计分员在解析语义学和综合依赖性分析等任务方面取得了成功。然而,在以结构化情绪分析等结构化分析为模式的任务中,“依赖边缘”非常稀少,这妨碍了分析员的性能。 因此,我们提出一个分散和模糊的注意力计分员,配有集合层,可以提高分析员的性能,并且将新的最新工艺设置在结构化情绪分析上。我们进一步探索结构化情绪分析模型的分解模式,与二级分析相提并论,并引入一个新的稀有的二阶边缘建筑程序,从而显著改善分析性能。
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