To promote and further develop RST-style discourse parsing models, we need a
strong baseline that can be regarded as a reference for reporting reliable
experimental results. This paper explores a strong baseline by integrating
existing simple parsing strategies, top-down and bottom-up, with various
transformer-based pre-trained language models. The experimental results
obtained from two benchmark datasets demonstrate that the parsing performance
strongly relies on the pretrained language models rather than the parsing
strategies. In particular, the bottom-up parser achieves large performance
gains compared to the current best parser when employing DeBERTa. We further
reveal that language models with a span-masking scheme especially boost the
parsing performance through our analysis within intra- and multi-sentential
parsing, and nuclearity prediction.

本篇论文探讨了一种强有力的基准线，通过将现有的简单解析策略（自上而下和自下而上）与各种基于 Transformer 的预先训练的语言模型进行集成，实现了 RST 风格的话语解析模型的推广和进一步发展。实验结果表明，解析性能强烈依赖预先训练的语言模型，尤其是 DeBERTa 对自下而上解析器表现的大幅提升。此外，我们还发现具有跨度屏蔽方案的语言模型特别能提高解析性能。