The nature of text-based data is not just unstructured but also, unlike regular signal processing or sequence modeling, cannot be arbitrarily down sampled. The assumptions behind every statistical model are defined for input-outputs where each data point (𝒙, 𝒚) for both inputs and outputs are defined on the same vector space, i.e., 𝒙 ∈ ℝ 𝑛 and 𝒚 ∈ ℝ 𝑚 , 𝑚, 𝑛 ≥ 1. In text-based data, individual sentences and each word in a sentence are of unequal lengths. Thus, very accurate transduction of text signals is imperative to not only convert each word to an accurate vector representation but also do in a way that the meaning of the sentence is captured. We applied the Closed Document Question Answering (CDQA) system to retrieve answers for the question we asked. CDQA is a part of information retrieval in the Question Answering (QA) system within the field of NLP that is capable of correctly answering questions from a given database of information. For this QA task, we use pre-trained Bidirectional Encoder Representations from Transformers (BERT) as a language model, which we train on our database. Our approach of implementing the CDQA system allows the luxury of getting the most relevant paragraphs from thousands of articles without having to manually annotate them. There are two main components of the CDQA system -Document Retriever and Document Reader. The Document Retriever identifies a list of candidate documents that are likeliest to contain answers to the given question. These documents are fed to the Document Reader (a pretrained deep-learning model, e.g., BERT) which gives the most probable answers. The answers are compared and ranked in the order of the scores. This is explained with a flow-diagram in Figure .