A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing

Abstract: 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 LLMs. The experimental results obtained from two benchmark datasets demonstrate that the parsing performance strongly relies on the pretrained LLMs 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 LLMs with a span-masking scheme especially boost the parsing performance through our analysis within intra- and multi-sentential parsing, and nuclearity prediction.