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Deal or No Deal?
End-to-End Learning for Negotiation Dialogues
1
9 NLP
2017 9 16 ( )
Mike Lewis, Denis Yarats, Yann N. Dauphin1, Devi Parikh, and Dhruv Batra
EMNLP 2017, pages 2433–2443
: http://aclweb.org/anthology/D17-1258
: https://github.com/facebookresearch/end-to-end-negotiator
※
2
AI
e.g.)
e.g.)
AI
end-to-end
DIALOGUE ROLLOUT
3
(
)
4
( )
( )
5
( )
6
( )
( )
( )
0
7
1. 10
2.
0
3. 0
8
Amazon Mechanical Turk
$0.15
$0.05
2236 5808
)
252 526
9
10
LIKELIHOOD
RL (Reinforcement
Learning)
ROLLOUTS
sequence to sequence
11LIKELIHOOD
12
13
g …
…o1..3
x0..T … ( )
"write:", "I", "want",
… "read:", "<choose>"
14LIKELIHOOD
g
GRUw
GRUg
GRU o
GRU!o
ht
hg
g ht GRU
h
!o
t 1 h
!o
t
h o
t+1h o
t 1
[Ext 1, hg]
[Ext, ht]
E
xt
xt
15LIKELIHOOD
p✓(xt|x0..t 1, g) / exp (ET
ht)
p✓(oi|x0..T , g) / exp (Woi
hs
)
o⇤
= arg max
o2O
Y
i
p✓(oi|x0..T , g)
16LIKELIHOOD
LIKELIHOOD
17RL
( )
rA
XA
A
A
µ LIKELIHOOD
18ROLLOUTS
u⇤R(u)
choose
o
"LIKELIHOOD " "RL " "ROLLOUTS "
"RL+ROLLOUTS "
" " "Perplexity"
19
( )252
526
20
RL+ROLLOUTS LIKELIHOOD
7.2 vs 5.3
(RL, ROLLOUTS)
LIKELIHOOD
RL, ROLLOUTS
LIKELIHOOD
21
22
23
end-to-end
24

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Deal or no deal? end to-end learning for negotiation dialogues