A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing
Naoki Kobayashi, Tsutomu Hirao, Hidetaka Kamigaito, Manabu Okumura, Masaaki Nagata · 2022
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.