[3J2-GS-6b] 言語メディア処理：対話

[3J2-GS-6b-05] How-to Tip Machine Comprehension with QA Examples collected from a Community QA Site

〇Tingxuan Li¹, Shuting Bai¹, Seiji Suzuki¹, Takehito Utsuro¹, Yasuhide Kawada² (1. University of Tsukuba, 2. Logworks Co., Ltd.)

Keywords:question answering, machine comprehension, tip, BERT, community QA

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.