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

Text discourse parsing plays an important role in understanding information
flow and argumentative structure in natural language. Previous research under
the Rhetorical Structure Theory (RST) has mostly focused on inducing and
evaluating models from the English treebank. However, the parsing tasks for
other languages such as German, Dutch, and Portuguese are still challenging due
to the shortage of annotated data. In this work, we investigate two approaches
to establish a neural, cross-lingual discourse parser via: (1) utilizing
multilingual vector representations; and (2) adopting segment-level translation
of the source content. Experiment results show that both methods are effective
even with limited training data, and achieve state-of-the-art performance on
cross-lingual, document-level discourse parsing on all sub-tasks.

研究了通过利用多语言向量表示和采用源内容的分段级别翻译建立神经交叉语言篇章分析器的两种方法，并表明这两种方法即使在有限的训练数据下也有效，并且在所有子任务上实现了跨语言、文档级的篇章分析性能。