The biaffine parser of Dozat and Manning (2017) was successfully extended to
semantic dependency parsing (SDP) (Dozat and Manning, 2018). Its performance on
graphs is surprisingly high given that, without the constraint of producing a
tree, all arcs for a given sentence are predicted independently from each other
(modulo a shared representation of tokens). To circumvent such an independence
of decision, while retaining the O(n^2) complexity and highly parallelizable
architecture, we propose to use simple auxiliary tasks that introduce some form
of interdependence between arcs. Experiments on the three English acyclic
datasets of SemEval 2015 task 18 (Oepen et al., 2015), and on French deep
syntactic cyclic graphs (Ribeyre et al., 2014) show modest but systematic
performance gains on a near state-of-the-art baseline using transformer-based
contextualized representations. This provides a simple and robust method to
boost SDP performance.

使用简单的辅助任务来增加语义依存分析（SDP）性能，对英语无环数据集和法语深层语法循环图进行实验，显示了在基于 Transformer 的上下文表示的接近最新技术基线上的有限但系统性能改善，从而提供了一种简单而稳健的提高 SDP 性能的方法。

提升双仿射语义依存分析的辅助任务

Auxiliary Tasks to Boost Biaffine Semantic Dependency Parsing

Previous works on key information extraction from visually rich documents
(VRDs) mainly focus on labeling the text within each bounding box (i.e.,
semantic entity), while the relations in-between are largely unexplored. In
this paper, we adapt the popular dependency parsing model, the biaffine parser,
to this entity relation extraction task. Being different from the original
dependency parsing model which recognizes dependency relations between words,
we identify relations between groups of words with layout information instead.
We have compared different representations of the semantic entity, different
VRD encoders, and different relation decoders. The results demonstrate that our
proposed model achieves 65.96% F1 score on the FUNSD dataset. As for the
real-world application, our model has been applied to the in-house customs
data, achieving reliable performance in the production setting.

本文使用 biaffine 解析模型，通过布局信息提取视觉信息中的实体关系，成功运用于生产环境。

在信息丰富的文档中以依赖解析的方式提取实体关系

Entity Relation Extraction as Dependency Parsing in Visually Rich  Documents

In the deep learning (DL) era, parsing models are extremely simplified with
little hurt on performance, thanks to the remarkable capability of multi-layer
BiLSTMs in context representation. As the most popular graph-based dependency
parser due to its high efficiency and performance, the biaffine parser directly
scores single dependencies under the arc-factorization assumption, and adopts a
very simple local token-wise cross-entropy training loss. This paper for the
first time presents a second-order TreeCRF extension to the biaffine parser.
For a long time, the complexity and inefficiency of the inside-outside
algorithm hinder the popularity of TreeCRF. To address this issue, we propose
an effective way to batchify the inside and Viterbi algorithms for direct large
matrix operation on GPUs, and to avoid the complex outside algorithm via
efficient back-propagation. Experiments and analysis on 27 datasets from 13
languages clearly show that techniques developed before the DL era, such as
structural learning (global TreeCRF loss) and high-order modeling are still
useful, and can further boost parsing performance over the state-of-the-art
biaffine parser, especially for partially annotated training data. We release
our code at this https URL

本文在深度学习时代，首次提出将二阶 TreeCRF 扩展到 biaffine 句法分析器，通过 27 个数据集的实验和分析，显示出在部分有标记训练数据的情况下，表现结构学习（全局 TreeCRF 损失）和高阶建模依然有用，可以进一步提高句法分析性能。