Semantics as a Foreign Language
Gabriel Stanovsky, Ido Dagan · 2018
We propose a novel approach to semantic dependency parsing (SDP) by casting the task as an instance of multi-lingual machine translation, where each semantic representation is a different foreign dialect.To that end, we first generalize syntactic linearization techniques to account for the richer semantic dependency graph structure.Following, we design a neural sequence-to-sequence framework which can effectively recover our graph linearizations, performing almost on-par with previous SDP state-of-the-art while requiring less parallel training annotations.Beyond SDP, our linearization technique opens the door to integration of graph-based semantic representations as features in neural models for downstream applications.