Generation from Abstract Meaning Representation using Tree Transducers
Language generation from purely semantic representations is a challenging task. This paper addresses generating English from the Abstract Meaning Representation (AMR) consisting of re entrant graphs whose nodes are concepts and edges are relations. The new method is trained statistically from AMRannotated English and consists of two major steps: (i) generating an appropriate spanning tree for the AMR, and (ii) applying tree tostring transducers to generate English. The method relies on discriminative learning and an argument realization model to overcome data sparsity. Initial tests on held out data show good promise despite the complexity of the task. The system is available open source as part of JAMR at: http:/github.com/jflanigan/jamr
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