How-to Tip Machine Comprehension with QA Examples collected from a Community QA Site

Abstract

In the field of factoid question answering(QA), it is known that the state-of-the-art technology has achieved an accuracy comparable to human. However, in the area of non-factoid QA, there are only limited numbers of datasets for training QA models. So within the field of the non-factoid QA, we develop a dataset for training Japanese tip QA models. Although it can be shown that the trained Japanese tip QA model outperforms the factoid QA model, this thesis further aims at answering tip questions more closely related to daily lives. Specifically, we collect community QA examples from a community QA site and then apply the trained Japanese tip QA model to those community QA examples. Evaluation results again show that the trained tip QA model outperforms the factoid QA model when testing against those community QA examples.