## 1 Introduction  
Most natural-language-processing (NLP) tasks suffer performance degradation when encountering long complex sentences, such as abstract meaning representation (AMR) parsing [1], semantic dependency parsing [2], constituency parsing [3], semantic role labeling [4], machine translation [5], and text summarization [6]. The intuition to address this issue is first to decompose complex sentences and then re-link simple ones, which share similar ideas with tasks such as rhetorical-structure-theory (RST)-style discourse parsing (RST parsing) [7], split-and-rephrase (SPRP) [8], text simplification (TS) [9], simple sentence decomposition (SSD) ([PERSON] et al. have yet to name their task, and thus, we summarize their main idea into _simple-sentence-decomposition (SSD)_ for the convenience of writing) [10], etc. However, previous works with such intuitions to process complex sentences are not practical for semantic parsing tasks, such as AMR [11] and semantic dependency parsing [12]. RST parsing, aiming to extract the rhetorical relations among elementary discourse units (EDUs) [13] at a document level, is still an open problem, where the state-of-the-art model only achieves 55.4 and 80.4 _Parseval-Full_ scores for multi- and intra-sentential parsing, respectively. Besides, the blurry definitions of EDUs and the misalignments between rhetorical relations and semantic relations make RST parsing unsuitable for semantic parsing. The SPRP task maintains a big splitting granularity, where the outputs may still be complex sentences. The TS and SSD tasks, which decompose complex sentences into simple ones, cannot preserve the original semantics for rephrasing for simpler syntax (TS) or dropping discourse connectives (SSD). To avoid the deficiencies of the previous works summarized above, we propose a novel task, hierarchical clause annotation (HCA), based on the linguistic research of clause hierarchy [14], where clauses are fundamental text units centering on a verb phrase and sentences with multiple clauses form a complex hierarchy. Our HCA is a more-lightweight task at the sentence level, has explicit definitions of clauses and appropriate mappings between inter-clause and semantic relations (vs. RST parsing), and aims to annotate complex sentences into a clause hierarchy (vs. SPRP) without changing or dropping any semantics (vs. TS and SSD). To show the potentialities of HCA to facilitate semantic parsing with complex sentences, we demonstrate the HCA tree, AMR graph, and semantic dependency graph (SDG) of a complex sentence from the AMR 2.0 dataset ([[https://catalog.ldc.upenn.edu/LDC2017T10](https://catalog.ldc.upenn.edu/LDC2017T10)]([https://catalog.ldc.upenn.edu/LDC2017T10](https://catalog.ldc.upenn.edu/LDC2017T10)), accessed on 15 June 2021) in Figure 1:  
\(\left[If\;I\;do\;not\;check,\right]_{C_{1}}\left[I\;get\;very\;anxious, \right]_{C_{2}}\left[which\;does\;sort\;of\;go\;away\;after\right.\)  
15-30 \(\left.\mathit{mins},\right]_{C_{3}}\left[but\;of\;then\;the\; anxiety\;is\;so\;much\right]_{C_{4}}\left[that\;I\;can\;not\;wait\;that\;long.\right]_{C_{5}}\)  
The above sentence is segmented into five clauses \(C_{i}\), where:  
* \(C_{2}\) and \(C_{4}\) are coordinate and contrastive;
* \(C_{1}\) and \(C_{3}\) are conditional adverbial and relative clauses of \(C_{2}\), respectively;
* \(C_{5}\) is a resultative adverbial clause of \(C_{4}\). In the HCA tree, the coordinate relation \(But\) and clauses \(C_{i}\) are nodes, and subordinate relations are directed edges. As demonstrated in Figure 1, the HCA tree shares the same hierarchy with two semantic parsing representations, indicating the possibility of incorporating HCA's structural information into semantic parsing with complex sentences. Inspired by the similarities among the HCA tree, AMR graph, and SDG, we annotated the first HCA corpus with the guidance in previous works of crowdsourcing annotation [15; 16].