论文标题
使用图形卷积网络编码框架选区的句法选区路径
Encoding Syntactic Constituency Paths for Frame-Semantic Parsing with Graph Convolutional Networks
论文作者
论文摘要
我们研究了将句法信息从组成树集整合到框架 - 语义解析子任务中的神经模型的问题，即目标识别（TI），框架识别（FI）和语义角色标签（SRL）。我们使用图形卷积网络来学习成分的特定表示形式，以便将每个成分作为与其对应的生产语法规则进行介绍。我们利用这些表示形式在一个句子中为每个单词构建句法特征，计算为单词和树中特定于任务特定节点之间的所有组成部分的总和，例如SRL的目标谓词。当在Framenet 1.5上测试时，我们的方法分别提高了〜1％和〜3.5％点的Ti和SRL的最新结果（以Bert为输入获得+2.5％的额外点），同时在Conll05数据集上产生可比较的结果，可与其他Syntax-Waware-Waware-Waware-Waware系统。
We study the problem of integrating syntactic information from constituency trees into a neural model in Frame-semantic parsing sub-tasks, namely Target Identification (TI), FrameIdentification (FI), and Semantic Role Labeling (SRL). We use a Graph Convolutional Network to learn specific representations of constituents, such that each constituent is profiled as the production grammar rule it corresponds to. We leverage these representations to build syntactic features for each word in a sentence, computed as the sum of all the constituents on the path between a word and a task-specific node in the tree, e.g. the target predicate for SRL. Our approach improves state-of-the-art results on the TI and SRL of ~1%and~3.5% points, respectively (+2.5% additional points are gained with BERT as input), when tested on FrameNet 1.5, while yielding comparable results on the CoNLL05 dataset to other syntax-aware systems.
