Recent advances in discourse parsing performance create the impression that,
as in other NLP tasks, performance for high-resource languages such as English
is finally becoming reliable. In this paper we demonstrate that this is not the
case, and thoroughly investigate the impact of data diversity on RST parsing
stability. We show that state-of-the-art architectures trained on the standard
English newswire benchmark do not generalize well, even within the news domain.
Using the two largest RST corpora of English with text from multiple genres, we
quantify the impact of genre diversity in training data for achieving
generalization to text types unseen during training. Our results show that a
heterogeneous training regime is critical for stable and generalizable models,
across parser architectures. We also provide error analyses of model outputs
and out-of-domain performance. To our knowledge, this study is the first to
fully evaluate cross-corpus RST parsing generalizability on complete trees,
examine between-genre degradation within an RST corpus, and investigate the
impact of genre diversity in training data composition.

在研究中，我们证明了高资源语言（如英语）在 RST 解析方面的性能并没有变得可靠，而我们使用两个最大的英语 RST 语料库在新闻领域中研究了数据多样性对解析稳定性的影响，结果表明异构训练是获得稳定且可推广模型的关键。同时，我们还提供了模型输出的误差分析和跨领域性能的研究。