  Question Answering systems these days typically use template-based language
generation. Though adequate for a domain-specific task, these systems are too
restrictive and predefined for domain-independent systems. This paper proposes
a system that outputs a full-length answer given a question and the extracted
factoid answer (short spans such as named entities) as the input. Our system
uses constituency and dependency parse trees of questions. A transformer-based
Grammar Error Correction model GECToR (2020), is used as a post-processing step
for better fluency. We compare our system with (i) Modified Pointer Generator
(SOTA) and (ii) Fine-tuned DialoGPT for factoid questions. We also test our
approach on existential (yes-no) questions with better results. Our model
generates accurate and fluent answers than the state-of-the-art (SOTA)
approaches. The evaluation is done on NewsQA and SqUAD datasets with an
increment of 0.4 and 0.9 percentage points in ROUGE-1 score respectively. Also
the inference time is reduced by 85\% as compared to the SOTA. The improved
datasets used for our evaluation will be released as part of the research
contribution.
