Automatic Derivation of Semantic Representations for Thai Serial Verb Constructions: A Grammar-Based Approach

Deep semantic representations are useful for many NLU tasks (Droganova and Zeman, 2019; Schuster and Manning, 2016) . Manual annotation to build these representations is timeconsuming, and so automatic approaches are preferred (Droganova and Zeman, 2019; Bender et al., 2015) . This paper demonstrates how rich semantic representations can be automatically derived for Thai Serial Verb Constructions (SVCs), where the semantic relationship between component verbs is not immediately clear from the surface forms. I present the first fully-implemented, unified analysis for Thai SVCs, deriving appropriate semantic representations (MRS; Copestake et al., 2005) from syntactic features, implemented within a DELPH-IN computational grammar (Slayden, 2009) . This analysis increases verified coverage of SVCs by 73% and decreases ambiguity by 46%. The final grammar can be found at: https://github.com/VipashaB94/ThaiGrammar