Abinaya Govindan, Gyan Ranjan, and Amit Verma, Neuron7.ai, USA

Question Answering (QA) has been a well-researched NLP problem over the past few years. The ability for users to query through information content that is available in a range of formats - organized and unstructured - has become a requirement. This paper proposes to untangle factoid question answering targeting the Hi-Tech domain. This paper addresses issues faced during document question answering, such as document parsing, indexing and retrieval (identifying the relevant documents) as well as machine comprehension (extract spans of correct answers from the context). Our suggested solution provides a comprehensive pipeline comprised of document ingestion modules that handle a wide range of unstructured data across various sections of the document, such as textual, images, and tabular content. Our studies on a variety of “real-world” and domain-specific datasets show how current fine-tuned models are insufficient for this challenging task, and how our proposed pipeline is an effective alternative.

machine comprehension, document parser, question answering, information retrieval.