Text discourse parsing weighs importantly in understanding information flow
and argumentative structure in natural language, making it beneficial for
downstream tasks. While previous work significantly improves the performance of
RST discourse parsing, they are not readily applicable to practical use cases:
(1) EDU segmentation is not integrated into most existing tree parsing
frameworks, thus it is not straightforward to apply such models on newly-coming
data. (2) Most parsers cannot be used in multilingual scenarios, because they
are developed only in English. (3) Parsers trained from single-domain treebanks
do not generalize well on out-of-domain inputs. In this work, we propose a
document-level multilingual RST discourse parsing framework, which conducts EDU
segmentation and discourse tree parsing jointly. Moreover, we propose a
cross-translation augmentation strategy to enable the framework to support
multilingual parsing and improve its domain generality. Experimental results
show that our model achieves state-of-the-art performance on document-level
multilingual RST parsing in all sub-tasks.

本文提出一个基于文档级别的多语言 RST 话语分析框架，该框架将 EDU 分割和话语树解析结合在一起，并且引入了跨语言翻译增强策略，以支持多语言解析并改善其领域通用性，实验结果表明，该模型在所有子任务中实现了文档级别多语言 RST 解析的最新性能。

DMRST：面向文档级多语言 RST 篇章分割和解析的联合框架

DMRST: A Joint Framework for Document-Level Multilingual RST Discourse  Segmentation and Parsing

This paper describes the ICS PAS system which took part in CoNLL 2018 shared
task on Multilingual Parsing from Raw Text to Universal Dependencies. The
system consists of jointly trained tagger, lemmatizer, and dependency parser
which are based on features extracted by a biLSTM network. The system uses both
fully connected and dilated convolutional neural architectures. The novelty of
our approach is the use of an additional loss function, which reduces the
number of cycles in the predicted dependency graphs, and the use of
self-training to increase the system performance. The proposed system, i.e. ICS
PAS (Warszawa), ranked 3th/4th in the official evaluation obtaining the
following overall results: 73.02 (LAS), 60.25 (MLAS) and 64.44 (BLEX).

本文介绍了 ICS PAS 系统及其在 CoNLL 2018 共享任务中的性能表现，系统包括基于 biLSTM 网络的联合训练标签、词形还原器和依存解析器，采用全连接和扩张卷积神经结构，并使用附加损失函数和自训练等方法提升系统性能。

半监督神经系统用于标记、解析和词形还原

Semi-Supervised Neural System for Tagging, Parsing and Lematization

This paper describes our system (HIT-SCIR) submitted to the CoNLL 2018 shared
task on Multilingual Parsing from Raw Text to Universal Dependencies. We base
our submission on Stanford's winning system for the CoNLL 2017 shared task and
make two effective extensions: 1) incorporating deep contextualized word
embeddings into both the part of speech tagger and parser; 2) ensembling
parsers trained with different initialization. We also explore different ways
of concatenating treebanks for further improvements. Experimental results on
the development data show the effectiveness of our methods. In the final
evaluation, our system was ranked first according to LAS (75.84%) and
outperformed the other systems by a large margin.

本文描述了我们的系统（HIT-SCIR），该系统提交给 CoNLL 2018 共享任务，涉及从原始文本到通用依赖的多语言解析。我们基于斯坦福的获胜系统进行提交和进行了两个有效的扩展：1）将深度上下文化词嵌入到词性标记器和解析器中；2）集合使用不同初始化的解析器进行训练。我们还探索了不同的合并树库的方法以进行进一步的改进。开发数据上的实验结果显示了我们方法的有效性。在最后的评估中，我们的系统在 LAS（75.84％）上排名第一，并大幅跑赢了其他系统。