  In recent years, several high-performance conversational systems have been
proposed based on the Transformer encoder-decoder model. Although previous
studies analyzed the effects of the model parameters and the decoding method on
subjective dialogue evaluations with overall metrics, they did not analyze how
the differences of fine-tuning datasets affect on user's detailed impression.
In addition, the Transformer-based approach has only been verified for English,
not for such languages with large inter-language distances as Japanese. In this
study, we develop large-scale Transformer-based Japanese dialogue models and
Japanese chit-chat datasets to examine the effectiveness of the
Transformer-based approach for building chit-chat dialogue systems. We
evaluated and analyzed the impressions of human dialogues in different
fine-tuning datasets, model parameters, and the use of additional information.
