第7回 NIPS+読み会・関西 Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization (NIPS2018)
1. Generating Informative and Diverse
Conversational Responses via
Adversarial Information Maximization
(NIPS2018)
Yizhe Zhang, Michel Galley, Jianfeng Gao, Zhe Gan,
Xiujun Li, Chris Brockett, Bill Dolan
Microsoft Research, Redmond, WA, USA
紹介者:品川 政太朗(NAIST/RIKEN)
2018/11/11 2018ⒸSeitaro Shinagawa AHC-lab NAIST
※Figures without citation are quoted from the authors’ paper
1/25
2. 2018/11/11 2018ⒸSeitaro Shinagawa AHC-lab NAIST
自己紹介
Favorite model(?):
Interest:
Interaction between human and
machine
Research Topic:
Dialog based Image generation
1989 Born in Sapporo
2009-2015 Tohoku Univ.
2015- NAIST(Ph.D student)
2/25
7. 2018/11/11 2018ⒸSeitaro Shinagawa AHC-lab NAIST
著者らの注目点
diverseかつinformativeな応答を生成できるようにしたい
diverse
informative
• I don’t know.
• I haven’t clue.
• I haven’t the foggiest
etc...
I don’t know.
• I like music.
• I like jazz.
etc...
I like music.
発話:”What is your hobby?”に対しての応答例
7/25
13. 2018/11/11 2018ⒸSeitaro Shinagawa AHC-lab NAIST
余談:InfoGAN [Chen+, NIPS2016]
z
real/fakeGen Dis
real
fake
c
• c: discrete latent code
• z: vector derived from random noise
• c’: predicted latent code
learning to make c and G(z,c) highly correlated
c’
Maximize mutual information 𝐼 𝑐; 𝐺 𝑧, 𝑐
The point for disentanglement
13/25