Chinese Macro Discourse Parsing on Generative Fusion and Distant Supervision

Most previous studies on discourse parsing have utilized discriminative models to construct tree structures. However, these models tend to overlook the global perspective of the tree structure as a whole during the step-by-step top-down or bottom-up parsing process. To address this issue, we propose DP-GF, a macro Discourse Parser based on Generative Fusion, which considers discourse parsing from both process-oriented and result-oriented perspectives. Additionally, due to the small size of existing corpora and the difficulty in annotating macro discourse structures, DP-GF addresses the small-sample problems by proposing a distant supervision training method that transforms a relatively large-scale topic structure corpus into a high-quality silver-standard discourse structure corpus. Our experimental results on MCDTB 2.0 demonstrate that our proposed model outperforms the state-of-the-art baselines on discourse tree construction.