Rhetorical structure trees have been shown to be useful for several
document-level tasks including summarization and document classification.
Previous approaches to RST parsing have used discriminative models; however,
these are less sample efficient than generative models, and RST parsing
datasets are typically small. In this paper, we present the first generative
model for RST parsing. Our model is a document-level RNN grammar (RNNG) with a
bottom-up traversal order. We show that, for our parser's traversal order,
previous beam search algorithms for RNNGs have a left-branching bias which is
ill-suited for RST parsing. We develop a novel beam search algorithm that keeps
track of both structure- and word-generating actions without exhibiting this
branching bias and results in absolute improvements of 6.8 and 2.9 on
unlabelled and labelled F1 over previous algorithms. Overall, our generative
model outperforms a discriminative model with the same features by 2.6 F1
points and achieves performance comparable to the state-of-the-art,
outperforming all published parsers from a recent replication study that do not
use additional training data.

本文提出了第一个用生成模型进行 RST 解析的文档级 RNN 语法，通过一种新的 beam search 算法，在而不展示左分支的偏差下，在未标注和标注的 F1 值上分别提高 6.8 和 2.9，超越了所有不使用额外训练数据的最新复制研究中发布的解析器，并且优于具有相同特征的判别模型 2.6F1 分。