We propose a novel linearization of a constituent tree, together with a new
locally normalized model. For each split point in a sentence, our model
computes the normalizer on all spans ending with that split point, and then
predicts a tree span from them. Compared with global models, our model is fast
and parallelizable. Different from previous local models, our linearization
method is tied on the spans directly and considers more local features when
performing span prediction, which is more interpretable and effective.
Experiments on PTB (95.8 F1) and CTB (92.4 F1) show that our model
significantly outperforms existing local models and efficiently achieves
competitive results with global models.

本文提出了一种新的组成树线性化方法和一种本地归一化的模型，用于句子中的每个分裂点，该模型在以该分割点结尾的所有子句上计算正则化值，并从中预测出一个树状子句。该模型相比全局模型更快、更可并行化，而且线性化方法直接绑定在子句上，考虑了更多的本地特征，更易于解释和有效。实验表明，该模型在 PTB（95.8 F1）和 CTB（92.4 F1）上显著优于现有的本地模型，且可以高效地获得与全局模型相同的竞争结果。

基于 Span 的组成树线性化

A Span-based Linearization for Constituent Trees

This paper describes a simple UCCA semantic graph parsing approach. The key
idea is to convert a UCCA semantic graph into a constituent tree, in which
extra labels are deliberately designed to mark remote edges and discontinuous
nodes for future recovery. In this way, we can make use of existing syntactic
parsing techniques. Based on the data statistics, we recover discontinuous
nodes directly according to the output labels of the constituent parser and use
a biaffine classification model to recover the more complex remote edges. The
classification model and the constituent parser are simultaneously trained
under the multi-task learning framework. We use the multilingual BERT as extra
features in the open tracks. Our system ranks the first place in the six
English/German closed/open tracks among seven participating systems. For the
seventh cross-lingual track, where there is little training data for French, we
propose a language embedding approach to utilize English and German training
data, and our result ranks the second place.

本文介绍了一种简单的 UCCA 语义图解析方法，将 UCCA 语义图转换为组成树，使用设计的额外标签作为未来恢复远程边缘和不连续节点的标记，并使用现有的句法分析技术。此系统在七个参与系统中的六个英语 / 德语闭合 / 开放跟踪中排名第一，在第七个跨语言跟踪中，它通过使用英语和德语培训数据的语言嵌入方法，将我们的结果排名第二。