Developing machine reading comprehension methods to automate financial report audit

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Neural network models have pushed the boundaries of NLP research in the last few years, improving state-of-the-art performances on a variety of tasks, including natural language understanding and machine reading comprehension. One of the main challenges in Natural Language Processing is dealing with the richness and variety of expressions in human languages. The goal of our industrial partner (Ark Paradigm) is to develop a method for automatically verifying the completeness of the corporate financial reports. Due to errors and omissions, the cost of manually or algorithmically verifying information continues to become more burdensome as legislative requirements increase each year, making the process more complex, time consuming and costly. With an objective of improving and building upon their existing product, the company seeks to break prevailing trade-offs between speed, cost, and quality under current approaches to machine reading comprehension.In this proposal, we approach the problem of natural language understanding as a machine reading comprehension problem. Reading comprehension in Machine Learning involves training a model that would be capable of processing a document, making inferences based on the given document and external knowledge, and answering complex questions with respect to the document's content. The proposed project is expected to make novel contributions to the research on machine reading comprehension, specifically in the financial domain. The financial domain offers unique challenges, therefore the methods that work on the existing reading comprehension datasets may not work as well on the financial domain. The development of machine reading comprehension methods that work well in this domain will be a useful and innovative contribution to this research field, as well as the finance industry.