We introduce the COMBO-based approach for EUD parsing and its implementation,
which took part in the IWPT 2021 EUD shared task. The goal of this task is to
parse raw texts in 17 languages into Enhanced Universal Dependencies (EUD). The
proposed approach uses COMBO to predict UD trees and EUD graphs. These
structures are then merged into the final EUD graphs. Some EUD edge labels are
extended with case information using a single language-independent expansion
rule. In the official evaluation, the solution ranked fourth, achieving an
average ELAS of 83.79%. The source code is available at
this https URL

该研究介绍了基于 COMBO 的 EUD 分析方法及其实现，其在 IWPT 2021 EUD 共享任务中获得了第四名，平均 ELAS 为 83.79％。

COMBO: 一种新的 EUD 解析模块

COMBO: a new module for EUD parsing

In this paper, we address the representation of coordinate constructions in
Enhanced Universal Dependencies (UD), where relevant dependency links are
propagated from conjunction heads to other conjuncts. English treebanks for
enhanced UD have been created from gold basic dependencies using a heuristic
rule-based converter, which propagates only core arguments. With the aim of
determining which set of links should be propagated from a semantic
perspective, we create a large-scale dataset of manually edited syntax graphs.
We identify several systematic errors in the original data, and propose to also
propagate adjuncts. We observe high inter-annotator agreement for this semantic
annotation task. Using our new manually verified dataset, we perform the first
principled comparison of rule-based and (partially novel) machine-learning
based methods for conjunction propagation for English. We show that learning
propagation rules is more effective than hand-designing heuristic rules. When
using automatic parses, our neural graph-parser based edge predictor
outperforms the currently predominant pipelinesusing a basic-layer tree parser
plus converters.

本文探讨了 Enhanced Universal Dependencies 中坐标结构的表示，通过创造一个大规模的数据集，手动编辑语法图，确定了应该在语义角度传播哪些依赖链接，比较了基于规则和基于机器学习的方法，并提出了一种基于神经图解析器的边缘预测器，超越了目前主要的基于基本层树解析器加转换器的流水线。

英语增强型通用依存语法中协同结构的分析与计算建模

Coordinate Constructions in English Enhanced Universal Dependencies:  Analysis and Computational Modeling

We present K{\o}psala, the Copenhagen-Uppsala system for the Enhanced
Universal Dependencies Shared Task at IWPT 2020. Our system is a pipeline
consisting of off-the-shelf models for everything but enhanced graph parsing,
and for the latter, a transition-based graph parser adapted from Che et al.
(2019). We train a single enhanced parser model per language, using gold
sentence splitting and tokenization for training, and rely only on tokenized
surface forms and multilingual BERT for encoding. While a bug introduced just
before submission resulted in a severe drop in precision, its post-submission
fix would bring us to 4th place in the official ranking, according to average
ELAS. Our parser demonstrates that a unified pipeline is effective for both
Meaning Representation Parsing and Enhanced Universal Dependencies.

我们提出了 Køpsala 系统，它通过一个统一管道实现了增强型通用依赖关系和意义表示的解析，并利用适应自 Che 等人（2019）的基于转换的图形解析器来完成增强型图形解析。