This document provides extensive guidelines and examples for Rhetorical
Structure Theory (RST) annotation in Mandarin Chinese. The guideline is divided
into three sections. We first introduce preprocessing steps to prepare data for
RST annotation. Secondly, we discuss syntactic criteria to segment texts into
Elementary Discourse Units (EDUs). Lastly, we provide examples to define and
distinguish discourse relations in different genres. We hope that this
reference manual can facilitate RST annotations in Chinese and accelerate the
development of the RST framework across languages.

该文提供了关于中文语篇结构理论的标注的指南，并包含预处理、分割和定义篇章关系的步骤和示例，旨在促进 RST 框架的跨语言应用。

汉语话语注释参考手册

Chinese Discourse Annotation Reference Manual

A sentence may express sentiments on multiple aspects. When these aspects are
associated with different sentiment polarities, a model's accuracy is often
adversely affected. We observe that multiple aspects in such hard sentences are
mostly expressed through multiple clauses, or formally known as elementary
discourse units (EDUs), and one EDU tends to express a single aspect with
unitary sentiment towards that aspect. In this paper, we propose to consider
EDU boundaries in sentence modeling, with attentions at both word and EDU
levels. Specifically, we highlight sentiment-bearing words in EDU through
word-level sparse attention. Then at EDU level, we force the model to attend to
the right EDU for the right aspect, by using EDU-level sparse attention and
orthogonal regularization. Experiments on three benchmark datasets show that
our simple EDU-Attention model outperforms state-of-the-art baselines. Because
EDU can be automatically segmented with high accuracy, our model can be applied
to sentences directly without the need of manual EDU boundary annotation.

该论文提出了一种基于 EDU 边界的句子建模方法，旨在克服句子同时表达多个方面情感极性的问题。该模型通过在单词和 EDU 级别上进行稀疏注意力，突出表达情感的单词，并使用 EDU 级别稀疏注意力和正交正则化来确保模型在适当的 EDU 上关注正确的方面。实验证明该方法优于现有的基线算法，并能够直接应用于句子，无需手动标注 EDU 边界。

基于 EDU 层面注意力的方面情感分析

Aspect-based Sentiment Analysis through EDU-level Attentions

Due to its great importance in deep natural language understanding and
various down-stream applications, text-level parsing of discourse rhetorical
structure (DRS) has been drawing more and more attention in recent years.
However, all the previous studies on text-level discourse parsing adopt
bottom-up approaches, which much limit the DRS determination on local
information and fail to well benefit from global information of the overall
discourse. In this paper, we justify from both computational and perceptive
points-of-view that the top-down architecture is more suitable for text-level
DRS parsing. On the basis, we propose a top-down neural architecture toward
text-level DRS parsing. In particular, we cast discourse parsing as a recursive
split point ranking task, where a split point is classified to different levels
according to its rank and the elementary discourse units (EDUs) associated with
it are arranged accordingly. In this way, we can determine the complete DRS as
a hierarchical tree structure via an encoder-decoder with an internal stack.
Experimentation on both the English RST-DT corpus and the Chinese CDTB corpus
shows the great effectiveness of our proposed top-down approach towards
text-level DRS parsing.

本文提出一种基于自上而下的神经网络结构，将话语的分层结构解析为分裂点排序任务，并在英文 RST-DT 语料库和中文 CDTB 语料库上进行实验，证明了该方法在文本级语篇分析中的高效性。