论文标题
DRTS通过结构意识编码和解码解析
DRTS Parsing with Structure-Aware Encoding and Decoding
论文作者
论文摘要
话语表示树结构（DRTS）解析是一项新颖的语义解析任务，最近一直关注。可以通过神经序列到序列模型来实现最先进的性能，将树构造视为增量序列产生问题。在模型中忽略了诸如输入语法和部分输出的中间骨架之类的结构信息，这对于DRTS解析可能有用。在这项工作中，我们在编码器和解码器阶段都提出了一个结构感知模型，以整合结构信息，其中图形注意网络（GAT）被利用以有效建模。基准数据集的实验结果表明，我们提出的模型有效，可以在文献中获得最佳性能。
Discourse representation tree structure (DRTS) parsing is a novel semantic parsing task which has been concerned most recently. State-of-the-art performance can be achieved by a neural sequence-to-sequence model, treating the tree construction as an incremental sequence generation problem. Structural information such as input syntax and the intermediate skeleton of the partial output has been ignored in the model, which could be potentially useful for the DRTS parsing. In this work, we propose a structural-aware model at both the encoder and decoder phase to integrate the structural information, where graph attention network (GAT) is exploited for effectively modeling. Experimental results on a benchmark dataset show that our proposed model is effective and can obtain the best performance in the literature.
