1 2 2
2/37
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Neural Conversational Model (NCM)
NCM [Vinyals et al., 2015]
?
3/37
RNN
<sos>
<eos>
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NCM
→
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e.g.
Why-QA [Oh et al., 2013]
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2
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→
→
7/37
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1. 2.
3.
1. 2.
3.
NCM
e.g.
1
2
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1. 2.
3.
NCM 1
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1. 2.
3.
1. 2.
3.
NCM
(e.g.
) 1
2
→
-
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(
e ( (
( ()W (
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(e.g.
→ b)
→ →
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( )
…
…
( )
… …
( )
… …
Role Factored Tensor Model (RFTM) [Weber et al., 2018]
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+ …
W1
…
1
T
W2
…
2
T
15/37
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Copyright © 2019 Shohei Tanaka All Rights Reserved 16/37
NCM EncDec, HRED
1-best ( ),
/
260 Twitter
( 6 )
attention )
Attention Encoder, Decoder (
hidden state vector
Encoder-Decoder with Attention [Luong et al., 2015]
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Hierarchical Recurrent Encoder-Decoder [Serban et al., 2016]
(hierarchical) utterance encoder
context encoder
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Context Encoder Utterance Encoder
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NCM EncDec HRED EncDec HRED
12 % 70 %
• BLEU
N-gram
• NIST
BLEU
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• Vector Extrema
( es
ew d )
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BLEU, NIST, and extrema
NCM
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NCM BLEU NIST extrema
EncDec
1-best
HRED
1-best
1-best
• Dist-1, 2
N-gram
• Pointwise Mutual Information (PMI)
Dist, PMI
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Dist and Pointwise Mutual Information (PMI)
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NCM dist-1 dist-2 PMI
EncDec
1-best
HRED
1-best
NCM
v.s.
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HRED
HRED + /
2 2 2 2
• Word coherency
0 2 1 3 1
• Dialogue continuity
0 2 1
2 2
• 2
• 1
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Word Coherency
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28.62 37.47 33.91
0% 20% 40% 60% 80% 100%
Coherency
1-best neither Re-ranking w/o embedding
30.10 44.50 25.40
0% 20% 40% 60% 80% 100%
Coherency
1-best neither Re-ranking w/ embedding
Dialogue Continuity
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40.84 20.63 38.53
0% 20% 40% 60% 80% 100%
Continuity
1-best neither Re-ranking w/o embedding
35.50 26.30 38.20
0% 20% 40% 60% 80% 100%
Continuity
1-best neither Re-ranking w/ embedding
• Word coherency
• Dialogue continuity
(dist) (PMI)
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30/37
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(
5 %
% %5 +) +
5 % % 0
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/ ( )
26 19 51
(
26 + 19
= 45
02 2 )5 5
5 % ) 02 2
(
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/ ( ) ( )
8 14
4 90
5
8 + 14
= 22
1
1
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1 (1-best)
(Re-ranked) 1
2
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… (1-best)
(Re-ranked)
3 )
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( ) … … …
(1-best) (
(Re-ranked) (
36/37
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/
3 3 7
•
•
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