Common Sense-Based Reasoning Using External Knowledge for Question Answering

Much research has been conducted recently on machines with human-level reasoning ability. Unlike domain knowledge, common sense is an extensive knowledge of the real world, and is included in human reasoning ability in many cases. However, it is difficult for machines to learn common sense because of the lack of well-written data. ComonsenseQA is a dataset in the form of multiple-choice questions and answers created for training and evaluating common sense in machines. RoBERTa, a pre-trained language model that performs well in natural language processing tasks, also performs well in ComonsenseQA. However, its performance is still inadequate compared to human performance. In this paper, we propose a model for predicting the correct answer by searching for information called evidence necessary to answer questions from external knowledge and by using the evidence as a context. The proposed model is comprised of three stages: An external knowledge finder to explore the required information from external knowledge; a triple-to-sentence converter, which converts triple-shaped in-formation such as (table, AtLocation, rug) into sentence form based on the configured evidence; and a reasoning module that predicts the correct answer. The performance of our proposed model showed a performance of 80.84% on the CommonsenseQA validation data and 76.14% on the test data. The latter is an improvement of 4.04% over the baseline model RoBERTa.