Despite extensive research on parsing of English sentences into Abstraction
Meaning Representation (AMR) graphs, which are compared to gold graphs via the
Smatch metric, full-document parsing into a unified graph representation lacks
well-defined representation and evaluation. Taking advantage of a
super-sentential level of coreference annotation from previous work, we
introduce a simple algorithm for deriving a unified graph representation,
avoiding the pitfalls of information loss from over-merging and lack of
coherence from under-merging. Next, we describe improvements to the Smatch
metric to make it tractable for comparing document-level graphs, and use it to
re-evaluate the best published document-level AMR parser. We also present a
pipeline approach combining the top performing AMR parser and coreference
resolution systems, providing a strong baseline for future research.

本文介绍了一种简单的算法，可将超句级的共指标注转化为统一的图表达方式，避免了信息合并的信息损失和不协调的问题。通过改进 Smatch 度量方法，使其可以用于比较文档级别的图形，并用它来重新评估最佳发布的文档级 AMR 解析器，同时提供了一个将 AMR 解析器和共指消解系统相结合的管道方法，这为未来的研究提供了一个强大的基准。

DocAMR: 多句 AMR 表示和评估

DocAMR: Multi-Sentence AMR Representation and Evaluation

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。