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