A Reliable and Accurate Multiple Choice Question Answering System for Due Diligence

The problem of answering multiple choice questions, based on the content of documents has been studied extensively in the machine learning literature. We pose the due diligence problem, where lawyers study legal contracts and assess the risk in potential mergers and acquisitions, as a multiple choice question answering problem, based on the text of the contract. Existing frameworks for question answering are not suitable for this task, due to the inherent scarcity and imbalance in the legal contract data available for training. We propose a question answering system which first identifies the excerpt in the contract which potentially contains the answer to a given question, and then builds a multi-class classifier to choose the answer to the question, based on the content of this excerpt. Unlike existing question answering systems, the proposed system explicitly handles the imbalance in the data, by generating synthetic instances of the minority answer categories, using the Synthetic Minority Oversampling Technique. This ensures that the number of instances in all the classes are roughly equal to each other, thus leading to more accurate and reliable classification. We demonstrate that the proposed question answering system outperforms the existing systems with minimal amount of training data.