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In natural language generation, word ordering is the task of putting the words composing the output surface form in the correct grammatical order. In this paper, we propose to apply general learning-to-rank algorithms to the task of word ordering in the broader context of surface realization. The major contributions of this paper are: (i) the design of three deep neural architectures implementing pointwise, pairwise, and listwise approaches for ranking; (ii) the testing of these neuraldoi:10.3390/info12080337 fatcat:63yzlggmgfdzfili7t7q6tpody