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This paper proposes a novel neural model for the understudied task ofgenerating text from keywords. The model takes as input a set of un-orderedkeywords, and part-of-speech (POS) based template instructions. This makes itideal for surface realization in any NLG setup. The framework is based on theencode-attend-decode paradigm, where keywords and templates are encoded first,and the decoder judiciously attends over the contexts derived from the encodedkeywords and templates to generate the sentences. Training exploits weaksupervision, as the model trains on a large amount of labeled data withkeywords and POS based templates prepared through completely automatic means.Qualitative and quantitative performance analyses on publicly availabletest-data in various domains reveal our system's superiority over baselines,built using state-of-the-art neural machine translation and controllabletransfer techniques. Our approach is indifferent to the order of inputkeywords.

Template Controllable keywords-to-text Generation