A Framework for Leveraging Contextual Information in Automated Domain Specific Comprehension

When it comes to information, Enterprises today are seen as a black hole, a mass of it goes in but gets difficult to extract the practical knowledge out of it. An automated system that has the ability to consume this large mass of information and provide specific, knowledgeable, domain-oriented responses back, will go a long way in unlocking the value of this large-scale unstructured information. In a bid to enrich the answering system's accuracy in Machine Reading Comprehension (MRC), we propose a domain-specific Question Answers (QuAns) framework that specifically aims to auto-generate questions from a domain-based document using an improvised Sequence to Sequence (Seq2Seq) technique equipped with Attention and Copy mechanism. The generated questions are conditioned on a set of candidate answers, derived using a combination of heuristic-driven and graph-based techniques. Further, it also leverages the contextual information by pooling strategy to build an automated response system using a deep custom fine-tuned Bidirectional Encoder Representations from Transformers (BERT) framework and retrieving the top-k contexts for a user query. The evaluation of the QuAns architecture is performed in combination with human supervision as at times, the automated metrics like BLEU, Exact Match (EM), F1 score, etc. fail to gauge the diverse semantic and structural aspects of a generated response. Primarily, the proffered ensemble technique has leveraged the augmented domain knowledge to enrich the answering response efficacy and improving the EM and F1 score by 14.86% and 12.76% respectively over Vanilla BERT architecture. To enhance the user experience, the conversational system is equipped with Natural Language Generation (NLG) to present a human-readable response. Our architectural pipeline aims to provide a one-stop solution for the organizations in processing huge volumes of multidisciplinary data by significantly reducing the human introspection and the overhead cost.