## 2 Related Work  
### RST Parsing  
In a document, the clauses, sentences, and paragraphs are logically connected together to form a coherent discourse. RST [13] provides a general way to describe the relations among parts in a text and postulates a hierarchical discourse structure called the discourse tree (DT). The leaves of a DT can be a clause or a phrase without strict definitions, known as elementary discourse units (EDUs). Adjacent EDUs and higher-order spans are non-overlapping and connected hierarchically through coherence relations. Thus, coherence in discourse can be analyzed in terms of how a nucleus interacts with its surrounding satellites to communicate the relationships between the main ideas and related ideas.  
The RST-parsing task generally requires breaking the text into EDUs (i.e., the discourse segmentation task) and linking the EDUs into a DT (i.e., the discourse parsing task). For discourse segmentation, [PERSON] et al. [17] proposed a Transformer-based neural classifier that enhances contextualized word embeddings with hand-crafted features and achieved the current state-of-the-art performance in the DISRPT 2021 Shared Task on Discourse Unit Segmentation ([[https://sites.google.com/georgetown.edu/disrpt2021?pl=1](https://sites.google.com/georgetown.edu/disrpt2021?pl=1)]([https://sites.google.com/georgetown.edu/disrpt2021?pl=1](https://sites.google.com/georgetown.edu/disrpt2021?pl=1)), accessed on 1 April 2023). For discourse parsing, [PERSON] et al. [18] explored a strong baseline by integrating previous simple parsing strategies, top-down and bottom-up, with various Transformer-based pretrained language models (PLMs).  
Figure 1: Clauses \(C_{i}\) in (**a**) correspond to subgraphs \(G_{i}\) in (**b**,**c**), respectively. Colored directed edges in (**a**) are inter-clause relations, mapping the same-colored AMR nodes and edges in (**b**) and semantic dependencies in (**c**). Note that reentrant AMR relations in (**b**) introduced by the pronoun “I” are omitted to save space, as well as semantic dependencies between orphan tokens and the root token “I” in (**c**).  
Table 1 demonstrates the comparison between our HCA and RST parsing with exemplified sentences from the RST Discourse Tagging Reference Manual ([[https://www.isi.edu/~marcu/discourse/](https://www.isi.edu/~marcu/discourse/)]([https://www.isi.edu/~marcu/discourse/](https://www.isi.edu/~marcu/discourse/)), accessed on 20 March 2023). Although both tasks aim to extract a tree structure from input texts, their definitions of elementary units and target interrelations vary, leading to the following differences:  
* The elementary units of HCA are clauses, while those of RST parsing are EDUs, including clauses and phrases. The blurry definitions of an EDU may cause obstacles in RST parsing. For example, phrases _\"as a result of margin calls\"_ in (1) and _\"Despite some their considerable incomes and assets\"_ in (2) are segmented as EDUs, but not clauses due to the absence of a verb. Moreover, although the clause \"that they have made it\" functions as a predicative of the verb \"feel\", it cannot be annotated as an EDU.
* The rhetorical relations in RST parsing characterize the coherence among EDUs, and some cannot map to semantic relations. For example, the semantic relation between _\"feel\"_ and _\"that they have made it\"_ in (2) is not captured in RST parsing.  
In summary, the blurry definitions of EDUs and the misalignments between rhetorical and semantic relations make RST parsing unsuitable for semantic parsing compared with our HCA.