Core Semantic First: A Top-down Approach for AMR Parsing

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pages 3799–3809, Hong Kong, China, November 3–7, 2019. c⃝2019 Association for Computational Linguistics 3799 Core Semantic First: A Top-down Approach for AMR Parsing∗ Deng Cai The Chinese University of Hong Kong thisisjcykcd@gmail.com Wai Lam The Chinese University of Hong Kong wlam@se.cuhk.edu.hk Abstract We introduce a novel scheme for parsing a piece of text into its Abstract Meaning Repre- sentation (AMR): Graph Spanning based Pars- ing (GSP). One novel characteristic of GSP is that it constructs a parse graph incremen- tally in a top-down fashion. Starting from the root, at each step, a new node and its con- nections to existing nodes will be jointly pre- dicted. The output graph spans the nodes by the distance to the root, following the intuition of first grasping the main ideas then digging into more details. The core semantic first prin- ciple emphasizes capturing the main ideas of a sentence, which is of great interest. We evalu- ate our model on the latest AMR sembank and achieve the state-of-the-art performance in the sense that no heuristic graph re-categorization is adopted. More importantly, the experiments show that our parser is especially good at ob- taining the core semantics. 1 Introduction Abstract Meaning Representation (AMR) (Ba- narescu et al., 2013) is a semantic formalism that encodes the meaning of a sentence as a rooted la- beled directed graph. As illustrated by an example in Figure 1, AMR abstracts away from the surface forms in text, where the root serves as a rudimen- tary representation of the overall focus while the details are elaborated as the depth of the graph in- creases. AMR has been proved useful for many downstream NLP tasks, including text summariza- tion (Liu et al., 2015; Hardy and Vlachos, 2018) and question answering (Mitra and Baral, 2016). The task of AMR parsing is to map natural lan- guage strings to AMR semantic graphs automat- ically. Compared to constituent parsing (Zhang ∗The work described in this paper is substantially sup- ported by a grant from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project Code: 14204418). The first author is grateful for the discus- sions with Zhisong Zhang and Zhijiang Guo. strike-01 time sudden earthquake time manner ARG2 prosper-01 happiness op1 op2 big mod such mod Figure 1: AMR for the sentence “During a time of pros- perity and happiness, such a big earthquake suddenly struck.”, where the subgraphs close to the root repre- sent the core semantics. and Clark, 2009) and dependency parsing (K¨ubler et al., 2009), AMR parsing is considered more challenging due to the following characteristics: (1) The nodes in AMR have no explicit alignment to text tokens; (2) The graph structure is more complicated because of frequent reentrancies and non-projective arcs; (3) There is a large and sparse vocabulary of possible node types (concepts). Many methods for AMR parsing have been de- veloped in …

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