Both bottom-up and top-down strategies have been used for neural
transition-based constituent parsing. The parsing strategies differ in terms of
the order in which they recognize productions in the derivation tree, where
bottom-up strategies and top-down strategies take post-order and pre-order
traversal over trees, respectively. Bottom-up parsers benefit from rich
features from readily built partial parses, but lack lookahead guidance in the
parsing process; top-down parsers benefit from non-local guidance for local
decisions, but rely on a strong encoder over the input to predict a constituent
hierarchy before its construction.To mitigate both issues, we propose a novel
parsing system based on in-order traversal over syntactic trees, designing a
set of transition actions to find a compromise between bottom-up constituent
information and top-down lookahead information. Based on stack-LSTM, our
psycholinguistically motivated constituent parsing system achieves 91.8 F1 on
WSJ benchmark. Furthermore, the system achieves 93.6 F1 with supervised
reranking and 94.2 F1 with semi-supervised reranking, which are the best
results on the WSJ benchmark.

本研究提出了基于中序遍历的句法树的新型解析系统，该系统采用一组转移动作来平衡自底向上成分信息和自顶向下的前瞻信息，基于堆栈 LSTM 的心理语言学成分解析系统在 WSJ 基准测试上实现了 91.8 F1，并且经过有监督的重排序后可以达到 93.6 F1，经过半监督的重排序后，可以达到 94.2 F1。

中序转移基于成分句法分析

In-Order Transition-based Constituent Parsing

We develop a novel deep contour detection algorithm with a top-down fully
convolutional encoder-decoder network. Our proposed method, named TD-CEDN,
solves two important issues in this low-level vision problem: (1) learning
multi-scale and multi-level features; and (2) applying an effective top-down
refined approach in the networks. TD-CEDN performs the pixel-wise prediction by
means of leveraging features at all layers of the net. Unlike skip connections
and previous encoder-decoder methods, we first learn a coarse feature map after
the encoder stage in a feedforward pass, and then refine this feature map in a
top-down strategy during the decoder stage utilizing features at successively
lower layers. Therefore, the deconvolutional process is conducted stepwise,
which is guided by Deeply-Supervision Net providing the integrated direct
supervision. The above proposed technologies lead to a more precise and clearer
prediction. Our proposed algorithm achieved the state-of-the-art on the BSDS500
dataset (ODS F-score of 0.788), the PASCAL VOC2012 dataset (ODS F-score of
0.588), and and the NYU Depth dataset (ODS F-score of 0.735).

本文提出了一种名为 TD-CEDN 的深度轮廓检测算法，该算法采用自上而下的完全卷积编码器 - 解码器网络，并在多尺度、多级特征学习以及前向传播阶段获得原始特征图，然后采用自下而上的方式进行解码，并使用各层次的特征进行迭代式的反卷积操作来实现基于像素级别的预测，从而取得了在 BSDS500、PASCAL VOC2012 和 NYU Depth 数据集上的最优效果。