1 research outputs found
Commonsense Knowledge + BERT for Level 2 Reading Comprehension Ability Test
Commonsense knowledge plays an important role when we read. The performance
of BERT on SQuAD dataset shows that the accuracy of BERT can be better than
human users. However, it does not mean that computers can surpass the human
being in reading comprehension. CommonsenseQA is a large-scale dataset which is
designed based on commonsense knowledge. BERT only achieved an accuracy of
55.9% on it. The result shows that computers cannot apply commonsense knowledge
like human beings to answer questions. Comprehension Ability Test (CAT) divided
the reading comprehension ability at four levels. We can achieve human like
comprehension ability level by level. BERT has performed well at level 1 which
does not require common knowledge. In this research, we propose a system which
aims to allow computers to read articles and answer related questions with
commonsense knowledge like a human being for CAT level 2. This system consists
of three parts. Firstly, we built a commonsense knowledge graph; and then
automatically constructed the commonsense knowledge question dataset according
to it. Finally, BERT is combined with the commonsense knowledge to achieve the
reading comprehension ability at CAT level 2. Experiments show that it can pass
the CAT as long as the required common knowledge is included in the knowledge
base