Intelligent personal assistants (IPAs) and interactive question answering (IQA) systems frequently encounter incomplete follow-up questions. The incomplete follow-up questions only make sense when seen in conjunction with the conversation context: The previous question and answer. Thus, IQA and IPA systems need to utilize the conversation context in order to handle the incomplete follow-up questions and generate an appropriate response. In this work, we present a retrieval based sequence to sequence learning system that can generate the complete (or intended) question for an incomplete follow-up question (given the conversation context). We can train our system using only a small labeled dataset (with only a few thousand conversations), by decomposing the original problem into two simpler and independent problems. The first problem focuses solely on selecting the candidate complete questions from a library of question templates (built offline using the small labeled conversations dataset). In the second problem, we re-rank the selected candidate questions using a neural language model (trained on millions of unlabelled questions independently). Our system can achieve a BLEU score of 42.91, as compared to 29.11 using an existing generation based approach. We further demonstrate the utility of our system as a plug-in module to an existing QA pipeline. Our system when added as a plug-in module, enables Siri to achieve an improvement of 131.57% in answering incomplete follow-up questions.