Accurate polyglot semantic parsing with DAG grammars

Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3567–3580 November 16 - 20, 2020. c⃝2020 Association for Computational Linguistics 3567 Accurate polyglot semantic parsing with DAG grammars Federico Fancellu Akos K´ad´ar Ran Zhang Afsaneh Fazly Samsung AI Centre Toronto (SAIC Toronto) {federico.f, ran.zhang, a.fazly}@samsung.com Abstract Semantic parses are directed acyclic graphs (DAGs), but in practice most parsers treat them as strings or trees, mainly because models that predict graphs are far less understood. This simplification, however, comes at a cost: there is no guarantee that the output is a well-formed graph. A recent work by Fancellu et al. (2019) addressed this problem by proposing a graph- aware sequence model that utilizes a DAG grammar to guide graph generation. We sig- nificantly improve upon this work, by propos- ing a simpler architecture as well as more ef- ficient training and inference algorithms that can always guarantee the well-formedness of the generated graphs. Importantly, unlike Fancellu et al., our model does not require language-specific features, and hence can har- ness the inherent ability of DAG-grammar parsing in multilingual settings. We perform monolingual as well as multilingual experi- ments on the Parallel Meaning Bank (Abzian- idze et al., 2017). Our parser outperforms previous graph-aware models by a large mar- gin, and closes the performance gap between string-based and DAG-grammar parsing. 1 Introduction Semantic parsers map a natural language utterance into a machine-readable meaning representation, thus helping machines understand and perform in- ference and reasoning over natural language data. Various semantic formalisms have been explored as the target meaning representation for seman- tic parsing, including dependency-based composi- tional semantics (Liang et al., 2013), abstract mean- ing representation (AMR, Banarescu et al., 2013), minimum recursion semantics (MRS, Copestake et al., 2005), and discourse representation theory (DRT, Kamp, 1981). Despite meaningful differ- ences across formalisms or parsing models, a rep- resentation in any of these formalisms can be ex- b1 e1 b2 bar(e1) AGENT(e1, ‘speaker’) THEME(e1, x1) e2 b3 lock(e2) AGENT(e2, ‘speaker’) PATIENT(e2,x1) CONTINUATION(b2, b3) x1 b4 door(x1) Figure 1: The discourse representation structure for ‘We barred the door and locked it’. For ease of refer- ence in later figures, each box includes a variable cor- responding to the box itself, at top right in gray. pressed as a directed acyclic graph (DAG). Consider for instance the sentence ‘We barred the door and locked it’, whose meaning representa- tion as a Discourse Representation Structure (DRS) is shown in Figure 1. A DRS is usually repre- sented as a set of nested boxes (e.g. b1), containing variable-bound discourse referents (e.g. ‘lock(e2)’), semantic constants (e.g. ‘speaker’), predicates (e.g. AGENT) expressing relations between variables and constants, and discourse relations between the boxes (e.g. CONTINUATION). This representation can be expressed as a DAG by turning referents and constants into vertices, and predicates and dis- course relations into connecting edges, as shown in Figure 2. How can we parse a sentence into a DAG? Commonly-adopted approaches view graphs as strings (e.g. van Noord and Bos, 2017; van No- ord …

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