  In task-oriented conversation systems, natural language generation systems
that generate sentences with specific information related to conversation flow
are useful. Our study focuses on language generation by considering various
information representing the meaning of utterances as multiple conditions of
generation. NLG from meaning representations, the conditions for sentence
meaning, generally goes through two steps: sentence planning and surface
realization. However, we propose a simple one-stage framework to generate
utterances directly from MR (Meaning Representation). Our model is based on
GPT2 and generates utterances with flat conditions on slot and value pairs,
which does not need to determine the structure of the sentence. We evaluate
several systems in the E2E dataset with 6 automatic metrics. Our system is a
simple method, but it demonstrates comparable performance to previous systems
in automated metrics. In addition, using only 10\% of the data set without any
other techniques, our model achieves comparable performance, and shows the
possibility of performing zero-shot generation and expanding to other datasets.
