  Large-scale open-domain dialogue systems such as PLATO-2 have achieved
state-of-the-art scores in both English and Chinese. However, little work
explores whether such dialogue systems also work well in the Japanese language.
In this work, we create a large-scale Japanese dialogue dataset,
Dialogue-Graph, which contains 1.656 million dialogue data in a tree structure
from News, TV subtitles, and Wikipedia corpus. Then, we train PLATO-2 using
Dialogue-Graph to build a large-scale Japanese dialogue system, PLATO-JDS. In
addition, to improve the PLATO-JDS in the topic switch issue, we introduce a
topic-switch algorithm composed of a topic discriminator to switch to a new
topic when user input differs from the previous topic. We evaluate the user
experience by using our model with respect to four metrics, namely, coherence,
informativeness, engagingness, and humanness. As a result, our proposed
PLATO-JDS achieves an average score of 1.500 for the human evaluation with
human-bot chat strategy, which is close to the maximum score of 2.000 and
suggests the high-quality dialogue generation capability of PLATO-2 in
Japanese. Furthermore, our proposed topic-switch algorithm achieves an average
score of 1.767 and outperforms PLATO-JDS by 0.267, indicating its effectiveness
in improving the user experience of our system.

Computation and Language;Multimedia