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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 SDPdoi:10.18653/v1/d18-1263 dblp:conf/emnlp/StanovskyD18 fatcat:4blyps4sx5efxnn4tfc5o4fvz4