Authors:
Yuki Eizuka
1
;
Kazuo Hara
1
and
Ikumi Suzuki
2
Affiliations:
1
Yamagata University, 1-4-12 Kojirakawa-machi, Yamagata City, 990-8560, Japan
;
2
Nagasaki University, 1-14 Bunkyo, Nagasaki City, 852-8521, Japan
Keyword(s):
Generative Adversarial Networks, Small Training Data, Emoticons.
Abstract:
Emoticons such as (_̂)̂ are face-shaped symbol sequences that are used to express emotions in text. However, the number of emoticons is miniscule. To increase the number of emoticons, we created emoticons using SeqGANs, which are generative adversarial networks for generating sequences. However, the small number of emoticons means that few emoticons can be used as training data for SeqGANs. This is concerning because as SeqGANs underfit small training data, generating emoticons using SeqGANs is difficult. To address this problem, we duplicate the training data. We observed that emoticons can be generated when the duplication magnification is of an appropriate value. However, as a trade-off, it was also observed that SeqGANs overfit the training data, i.e., they produce emoticons that are exactly the same as the training data.