Despite recent advances in Natural Language Processing (NLP), hierarchical
discourse parsing in the framework of Rhetorical Structure Theory remains
challenging, and our understanding of the reasons for this are as yet limited.
In this paper, we examine and model some of the factors associated with parsing
difficulties in previous work: the existence of implicit discourse relations,
challenges in identifying long-distance relations, out-of-vocabulary items, and
more. In order to assess the relative importance of these variables, we also
release two annotated English test-sets with explicit correct and distracting
discourse markers associated with gold standard RST relations. Our results show
that as in shallow discourse parsing, the explicit/implicit distinction plays a
role, but that long-distance dependencies are the main challenge, while lack of
lexical overlap is less of a problem, at least for in-domain parsing. Our final
model is able to predict where errors will occur with an accuracy of 76.3% for
the bottom-up parser and 76.6% for the top-down parser.

通过研究探讨了自然语言处理中层次结构篇章分析在修辞结构理论框架中的困难，并建立了与之相关的因素模型，其中隐含的篇章关系、识别远程关系的挑战、词汇外的项目等是现有工作中的解析难点。通过发布两个带有明确正确和干扰篇章标记的英文测试集，以评估这些变量的相对重要性。研究结果表明，如同浅层篇章分析，明示 / 隐含区别起到了一定作用，但远程依赖是主要挑战，而词汇重叠的缺乏则并非问题，至少对于领域内解析而言。最终的模型能够 76.3% 的准确率预测底向上解析器的错误和 76.6% 的准确率预测顶向下解析器的错误。

英语 RST 解析中的难点是什么？通过预测模型进行错误分析

What's Hard in English RST Parsing? Predictive Models for Error Analysis

A lack of large-scale human-annotated data has hampered the hierarchical
discourse parsing of Chinese. In this paper, we present GCDT, the largest
hierarchical discourse treebank for Mandarin Chinese in the framework of
Rhetorical Structure Theory (RST). GCDT covers over 60K tokens across five
genres of freely available text, using the same relation inventory as
contemporary RST treebanks for English. We also report on this dataset's
parsing experiments, including state-of-the-art (SOTA) scores for Chinese RST
parsing and RST parsing on the English GUM dataset, using cross-lingual
training in Chinese and English with multilingual embeddings.

本文提出了 GCDT，这是目前最大的汉语层次化语篇树库，基于修辞结构理论，覆盖五种文本类型，使用与当代英语 RST 树库相同的关系清单。通过使用中英文多语言嵌入来训练语言关系，本文还介绍了这个数据集的分析实验，包括中英文 RST 解析和在英语 GUM 数据集上的 RST 解析，其中包括最先进（SOTA）的中文 RST 解析分数。