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Clustering
K-Means
Clustering
LSH Pair-wise
Clustering
DBSCAN
Algorithm
EM Clustering
Using GMM
Query
Answer
Input Data
Hyper-parameter Tuning
Dropout Input
Keep Probability
Dropout Output
Keep Probability
Dropout Intra
Keep Probability
Dropout State
Keep Probability
Max Epoch
Target
Perplexity
Batch Size
Unknown Count
Learning Rate
Unknown Ratio
Maximum
Gradient Norm
Unknown Percent
Encoder
Hidden sizes
Decoder
Hidden sizes
Buckets
Minimum
Vocab Count
Bidirectional
Bottleneck
Validation
Score
Validation
Score
Onehot Seq2Seq
Embed Seq2Seq
Global
Embedding Model
Validation
Score
AutoML Pipeline
Ensemble Weights
GRU
1 2Cell Bi-directional LSTM + Highway Network
4 Embedding Sharing
!"#$ … !"#&
!'#$
…!'#$<go>
…!'#(
1
5
3 4
4
62
7
8
LSTM
Bi-LSTM
Residual Block
6 Query Encoding Vector
7 Query Encoder
8 Answer Decoder
5 N-hot representation
Additional Model
Convolutional S2S
DSSMAdditive
Multiplicative
Attention Mechanism3
POS정보Token Index EOMI 정보
[밥 을 먹다] [N, JOSA, VERB] [0, 0, 다]
∑
Hidde
n=128
Token one-hot
d=# of vocabulary
POS tag one-hot
d=# of tag
Token endingone-hot
d=# of ending
Token
EmbeddingMatrix
POS tag
EmbeddingMatrix
Token ending
EmbeddingMatrix
Chatbot Builder
Chatbot Engine
Chatbot Service Serving
Deep Learning
Application
Resource
Manager
Node
Manager
Node
Manager
Node
Manager
Docker
RegistryZoo keeper
Training Data
Pre-Processing
…
Multi model
version management
and deploy
Data Input Data Output
1
1.5
2
2.5
3
3.5
4
0.2
0.22
0.24
0.26
0.28
0.3
12000 14000 16000 18000 20000 22000
Cluster	K	갯수
카페 고객센터 챗봇
자동평가 사람평가 기존점수
1
1.5
2
2.5
3
3.5
4
0.2
0.22
0.24
0.26
0.28
0.3
0.32
0.34
0.36
0.38
0.4
90 92 94 96 98 100 102 104 106
Cluster	K	갯수
XX	통운 고객센터 챗봇
자동평가 사람평가 기존점수
1
1.5
2
2.5
3
3.5
4
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
80000	 90000	 100000	 110000	 120000	 130000	 140000	
Cluster	K	갯수
클로바 한국어 대화
자동평가 사람평가 기존점수
unfold
Table
Lookup
Table
Lookup
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MatMul
MatMul
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MatMul
Weighting
Function ↵<latexit sha1_base64="Zp4VLrVWNPDm0sLggOD2KANdtmU=">AAACH3icZVDLSgNBEJw1PmJ8JXr0shgET2FXBD1GclD0EsFEwQTpnXTM6OzsMNMrhiX/4FVvfo038Zq/cRIXMVrQUFR1090VaSksBcHYmyvMLywuFZdLK6tr6xvlymbbJqnh2OKJTMx1BBalUNgiQRKvtUGII4lX0UNj4l89orEiUZc01NiN4U6JvuBATmp3QOoB3JarQS2Ywv9PwpxUWY7mbcUrdHoJT2NUxCVYexMGmroZGBJc4qjUSS1q4A9whzeOKojRdrPpuSN/1yk9v58YV4r8qfp7IoPY2mEcuc4YaGD/ehPxx9udWUX9o24mlE4JFf/e1E+lT4k/ed7vCYOc5NAR4Ea4Y30+AAOcXEQza56wcXbu/rBIjsQg1OTW7FhriScJDQQflVxs4d+Q/pP2fi0MauHFQbV+kAdYZNtsh+2xkB2yOjtlTdZinN2zZ/bCXr0379378D6/W+e8fGaLzcAbfwGO66G2</latexit><latexit sha1_base64="Zp4VLrVWNPDm0sLggOD2KANdtmU=">AAACH3icZVDLSgNBEJw1PmJ8JXr0shgET2FXBD1GclD0EsFEwQTpnXTM6OzsMNMrhiX/4FVvfo038Zq/cRIXMVrQUFR1090VaSksBcHYmyvMLywuFZdLK6tr6xvlymbbJqnh2OKJTMx1BBalUNgiQRKvtUGII4lX0UNj4l89orEiUZc01NiN4U6JvuBATmp3QOoB3JarQS2Ywv9PwpxUWY7mbcUrdHoJT2NUxCVYexMGmroZGBJc4qjUSS1q4A9whzeOKojRdrPpuSN/1yk9v58YV4r8qfp7IoPY2mEcuc4YaGD/ehPxx9udWUX9o24mlE4JFf/e1E+lT4k/ed7vCYOc5NAR4Ea4Y30+AAOcXEQza56wcXbu/rBIjsQg1OTW7FhriScJDQQflVxs4d+Q/pP2fi0MauHFQbV+kAdYZNtsh+2xkB2yOjtlTdZinN2zZ/bCXr0379378D6/W+e8fGaLzcAbfwGO66G2</latexit><latexit sha1_base64="Zp4VLrVWNPDm0sLggOD2KANdtmU=">AAACH3icZVDLSgNBEJw1PmJ8JXr0shgET2FXBD1GclD0EsFEwQTpnXTM6OzsMNMrhiX/4FVvfo038Zq/cRIXMVrQUFR1090VaSksBcHYmyvMLywuFZdLK6tr6xvlymbbJqnh2OKJTMx1BBalUNgiQRKvtUGII4lX0UNj4l89orEiUZc01NiN4U6JvuBATmp3QOoB3JarQS2Ywv9PwpxUWY7mbcUrdHoJT2NUxCVYexMGmroZGBJc4qjUSS1q4A9whzeOKojRdrPpuSN/1yk9v58YV4r8qfp7IoPY2mEcuc4YaGD/ehPxx9udWUX9o24mlE4JFf/e1E+lT4k/ed7vCYOc5NAR4Ea4Y30+AAOcXEQza56wcXbu/rBIjsQg1OTW7FhriScJDQQflVxs4d+Q/pP2fi0MauHFQbV+kAdYZNtsh+2xkB2yOjtlTdZinN2zZ/bCXr0379378D6/W+e8fGaLzcAbfwGO66G2</latexit><latexit sha1_base64="Zp4VLrVWNPDm0sLggOD2KANdtmU=">AAACH3icZVDLSgNBEJw1PmJ8JXr0shgET2FXBD1GclD0EsFEwQTpnXTM6OzsMNMrhiX/4FVvfo038Zq/cRIXMVrQUFR1090VaSksBcHYmyvMLywuFZdLK6tr6xvlymbbJqnh2OKJTMx1BBalUNgiQRKvtUGII4lX0UNj4l89orEiUZc01NiN4U6JvuBATmp3QOoB3JarQS2Ywv9PwpxUWY7mbcUrdHoJT2NUxCVYexMGmroZGBJc4qjUSS1q4A9whzeOKojRdrPpuSN/1yk9v58YV4r8qfp7IoPY2mEcuc4YaGD/ehPxx9udWUX9o24mlE4JFf/e1E+lT4k/ed7vCYOc5NAR4Ea4Y30+AAOcXEQza56wcXbu/rBIjsQg1OTW7FhriScJDQQflVxs4d+Q/pP2fi0MauHFQbV+kAdYZNtsh+2xkB2yOjtlTdZinN2zZ/bCXr0379378D6/W+e8fGaLzcAbfwGO66G2</latexit>
RN
Summation
⇥<latexit sha1_base64="4Fb9A5QAQZYCkPKTtzP6rGQNJTc=">AAACH3icZVBNSwJRFH2jfZh9aS3bDInQSmZCqKXhoqiNQX6Airx5XvXlmzfDe3ciGfwPbWvXr2kXbf03PXWI1AsXDufeyzn3eKHgGh1nZqXSW9s7u5m97P7B4dFxLn/S0EGkGNRZIALV8qgGwSXUkaOAVqiA+p6ApjeuzufNF1CaB/IJJyF0fTqUfMAZRUM1Osh90L1cwSk5i7I3gZuAAkmq1stb6U4/YJEPEpmgWrddJ8RuTBVyJmCa7UQaQsrGdAhtAyU1Kt14YXdqFw3TtweBMi3RXrD/L2Lqaz3xPbPpUxzp9dmc/JsVV6RwcN2NuQwjBMmWSoNI2BjY8+ftPlfAUEwMoExxY9ZmI6ooQxPRiswrVO8fzB8a0ACfcjn3Gt+EoYDbAEecTbMmNnc9pE3QuCy5Tsl9LBcq5STADDkj5+SCuOSKVMgdqZE6YeSZvJF38mF9Wl/Wt/WzXE1Zyc0pWSlr9gu/DaHS</latexit><latexit sha1_base64="4Fb9A5QAQZYCkPKTtzP6rGQNJTc=">AAACH3icZVBNSwJRFH2jfZh9aS3bDInQSmZCqKXhoqiNQX6Airx5XvXlmzfDe3ciGfwPbWvXr2kXbf03PXWI1AsXDufeyzn3eKHgGh1nZqXSW9s7u5m97P7B4dFxLn/S0EGkGNRZIALV8qgGwSXUkaOAVqiA+p6ApjeuzufNF1CaB/IJJyF0fTqUfMAZRUM1Osh90L1cwSk5i7I3gZuAAkmq1stb6U4/YJEPEpmgWrddJ8RuTBVyJmCa7UQaQsrGdAhtAyU1Kt14YXdqFw3TtweBMi3RXrD/L2Lqaz3xPbPpUxzp9dmc/JsVV6RwcN2NuQwjBMmWSoNI2BjY8+ftPlfAUEwMoExxY9ZmI6ooQxPRiswrVO8fzB8a0ACfcjn3Gt+EoYDbAEecTbMmNnc9pE3QuCy5Tsl9LBcq5STADDkj5+SCuOSKVMgdqZE6YeSZvJF38mF9Wl/Wt/WzXE1Zyc0pWSlr9gu/DaHS</latexit><latexit sha1_base64="4Fb9A5QAQZYCkPKTtzP6rGQNJTc=">AAACH3icZVBNSwJRFH2jfZh9aS3bDInQSmZCqKXhoqiNQX6Airx5XvXlmzfDe3ciGfwPbWvXr2kXbf03PXWI1AsXDufeyzn3eKHgGh1nZqXSW9s7u5m97P7B4dFxLn/S0EGkGNRZIALV8qgGwSXUkaOAVqiA+p6ApjeuzufNF1CaB/IJJyF0fTqUfMAZRUM1Osh90L1cwSk5i7I3gZuAAkmq1stb6U4/YJEPEpmgWrddJ8RuTBVyJmCa7UQaQsrGdAhtAyU1Kt14YXdqFw3TtweBMi3RXrD/L2Lqaz3xPbPpUxzp9dmc/JsVV6RwcN2NuQwjBMmWSoNI2BjY8+ftPlfAUEwMoExxY9ZmI6ooQxPRiswrVO8fzB8a0ACfcjn3Gt+EoYDbAEecTbMmNnc9pE3QuCy5Tsl9LBcq5STADDkj5+SCuOSKVMgdqZE6YeSZvJF38mF9Wl/Wt/WzXE1Zyc0pWSlr9gu/DaHS</latexit><latexit sha1_base64="4Fb9A5QAQZYCkPKTtzP6rGQNJTc=">AAACH3icZVBNSwJRFH2jfZh9aS3bDInQSmZCqKXhoqiNQX6Airx5XvXlmzfDe3ciGfwPbWvXr2kXbf03PXWI1AsXDufeyzn3eKHgGh1nZqXSW9s7u5m97P7B4dFxLn/S0EGkGNRZIALV8qgGwSXUkaOAVqiA+p6ApjeuzufNF1CaB/IJJyF0fTqUfMAZRUM1Osh90L1cwSk5i7I3gZuAAkmq1stb6U4/YJEPEpmgWrddJ8RuTBVyJmCa7UQaQsrGdAhtAyU1Kt14YXdqFw3TtweBMi3RXrD/L2Lqaz3xPbPpUxzp9dmc/JsVV6RwcN2NuQwjBMmWSoNI2BjY8+ftPlfAUEwMoExxY9ZmI6ooQxPRiswrVO8fzB8a0ACfcjn3Gt+EoYDbAEecTbMmNnc9pE3QuCy5Tsl9LBcq5STADDkj5+SCuOSKVMgdqZE6YeSZvJF38mF9Wl/Wt/WzXE1Zyc0pWSlr9gu/DaHS</latexit>
An arbitrary
sub-graph <latexit sha1_base64="6O8l3m0jAPTtFPUg7uyLhsP+li4=">AAACHXicZVBNSwMxEE38rPVbj14Wi+Cp7Iqgx4oHRS8VrC20pWTTqRvMZkMyKy1L/4JXvflrvIlX8d+YrYtY+2Dg8d4MM/NCLYVF3/+ic/MLi0vLpZXy6tr6xubW9s6dTVLDocETmZhWyCxIoaCBAiW0tAEWhxKa4cN57jcfwViRqFscaejG7F6JgeAMc6kDQ93bqvhVfwJvlgQFqZAC9d42ne/0E57GoJBLZm078DV2M2ZQcAnjcie1oBl/YPfQdlSxGGw3mxw79g6c0vcGiXGl0JuofycyFls7ikPXGTOM7H8vF3+9g6lVODjtZkLpFEHxn02DVHqYePnrXl8Y4ChHjjBuhDvW4xEzjKMLaGrNEM6vrt0fFtCRmAmV35qdaS3hIsFI8HHZxRb8D2mW3B1VA78a3BxXasdFgCWyR/bJIQnICamRS1InDcJJRJ7IM3mhr/SNvtOPn9Y5WszskinQz28ElqDp</latexit><latexit sha1_base64="6O8l3m0jAPTtFPUg7uyLhsP+li4=">AAACHXicZVBNSwMxEE38rPVbj14Wi+Cp7Iqgx4oHRS8VrC20pWTTqRvMZkMyKy1L/4JXvflrvIlX8d+YrYtY+2Dg8d4MM/NCLYVF3/+ic/MLi0vLpZXy6tr6xubW9s6dTVLDocETmZhWyCxIoaCBAiW0tAEWhxKa4cN57jcfwViRqFscaejG7F6JgeAMc6kDQ93bqvhVfwJvlgQFqZAC9d42ne/0E57GoJBLZm078DV2M2ZQcAnjcie1oBl/YPfQdlSxGGw3mxw79g6c0vcGiXGl0JuofycyFls7ikPXGTOM7H8vF3+9g6lVODjtZkLpFEHxn02DVHqYePnrXl8Y4ChHjjBuhDvW4xEzjKMLaGrNEM6vrt0fFtCRmAmV35qdaS3hIsFI8HHZxRb8D2mW3B1VA78a3BxXasdFgCWyR/bJIQnICamRS1InDcJJRJ7IM3mhr/SNvtOPn9Y5WszskinQz28ElqDp</latexit><latexit sha1_base64="6O8l3m0jAPTtFPUg7uyLhsP+li4=">AAACHXicZVBNSwMxEE38rPVbj14Wi+Cp7Iqgx4oHRS8VrC20pWTTqRvMZkMyKy1L/4JXvflrvIlX8d+YrYtY+2Dg8d4MM/NCLYVF3/+ic/MLi0vLpZXy6tr6xubW9s6dTVLDocETmZhWyCxIoaCBAiW0tAEWhxKa4cN57jcfwViRqFscaejG7F6JgeAMc6kDQ93bqvhVfwJvlgQFqZAC9d42ne/0E57GoJBLZm078DV2M2ZQcAnjcie1oBl/YPfQdlSxGGw3mxw79g6c0vcGiXGl0JuofycyFls7ikPXGTOM7H8vF3+9g6lVODjtZkLpFEHxn02DVHqYePnrXl8Y4ChHjjBuhDvW4xEzjKMLaGrNEM6vrt0fFtCRmAmV35qdaS3hIsFI8HHZxRb8D2mW3B1VA78a3BxXasdFgCWyR/bJIQnICamRS1InDcJJRJ7IM3mhr/SNvtOPn9Y5WszskinQz28ElqDp</latexit><latexit sha1_base64="6O8l3m0jAPTtFPUg7uyLhsP+li4=">AAACHXicZVBNSwMxEE38rPVbj14Wi+Cp7Iqgx4oHRS8VrC20pWTTqRvMZkMyKy1L/4JXvflrvIlX8d+YrYtY+2Dg8d4MM/NCLYVF3/+ic/MLi0vLpZXy6tr6xubW9s6dTVLDocETmZhWyCxIoaCBAiW0tAEWhxKa4cN57jcfwViRqFscaejG7F6JgeAMc6kDQ93bqvhVfwJvlgQFqZAC9d42ne/0E57GoJBLZm078DV2M2ZQcAnjcie1oBl/YPfQdlSxGGw3mxw79g6c0vcGiXGl0JuofycyFls7ikPXGTOM7H8vF3+9g6lVODjtZkLpFEHxn02DVHqYePnrXl8Y4ChHjjBuhDvW4xEzjKMLaGrNEM6vrt0fFtCRmAmV35qdaS3hIsFI8HHZxRb8D2mW3B1VA78a3BxXasdFgCWyR/bJIQnICamRS1InDcJJRJ7IM3mhr/SNvtOPn9Y5WszskinQz28ElqDp</latexit>
X
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Table
Lookup
Table
Lookup
Table
Lookup
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Average
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Softmax
Type Default Value Type Default Value
Max Epoch 100000 Dropout Input Keep Probability 0.8
Batch Size 1024 Dropout Intra Keep Probability 0.5
Unknown Count 3 Dropout Output Keep Probability 0.8
Target Perplexity 6.5 Dropout State Keep Probability 0.75
Threshold 0.963 Buckets [8, 13]
Unk Ratio 0.333 Hidden Sizes [64, 64, 32] & [32, 64, 128]
Learning Rate 0.0005 Bottleneck FALSE
Unknown Percent 0.33 Bidirectional TRUE
Max Gradient Norm 5 Min Vocab Count 2
0.00241 0.00373 0.00236 0.00977 0.00598 0.00325 0.01231 0.02275 0.00433 0.00562 0.00681 0.00646 0.00762 0.00821 0.00945 0.01089 0.00809 0.01020 0.01464
( )
(*
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[211] 인공지능이 인공지능 챗봇을 만든다

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  • 21. Clustering K-Means Clustering LSH Pair-wise Clustering DBSCAN Algorithm EM Clustering Using GMM Query Answer Input Data Hyper-parameter Tuning Dropout Input Keep Probability Dropout Output Keep Probability Dropout Intra Keep Probability Dropout State Keep Probability Max Epoch Target Perplexity Batch Size Unknown Count Learning Rate Unknown Ratio Maximum Gradient Norm Unknown Percent Encoder Hidden sizes Decoder Hidden sizes Buckets Minimum Vocab Count Bidirectional Bottleneck Validation Score Validation Score Onehot Seq2Seq Embed Seq2Seq Global Embedding Model Validation Score AutoML Pipeline Ensemble Weights
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  • 28. GRU 1 2Cell Bi-directional LSTM + Highway Network 4 Embedding Sharing !"#$ … !"#& !'#$ …!'#$<go> …!'#( 1 5 3 4 4 62 7 8 LSTM Bi-LSTM Residual Block 6 Query Encoding Vector 7 Query Encoder 8 Answer Decoder 5 N-hot representation Additional Model Convolutional S2S DSSMAdditive Multiplicative Attention Mechanism3
  • 29. POS정보Token Index EOMI 정보 [밥 을 먹다] [N, JOSA, VERB] [0, 0, 다] ∑ Hidde n=128 Token one-hot d=# of vocabulary POS tag one-hot d=# of tag Token endingone-hot d=# of ending Token EmbeddingMatrix POS tag EmbeddingMatrix Token ending EmbeddingMatrix
  • 30.
  • 31. Chatbot Builder Chatbot Engine Chatbot Service Serving Deep Learning Application Resource Manager Node Manager Node Manager Node Manager Docker RegistryZoo keeper Training Data Pre-Processing … Multi model version management and deploy
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  • 47. Data Input Data Output
  • 48. 1 1.5 2 2.5 3 3.5 4 0.2 0.22 0.24 0.26 0.28 0.3 12000 14000 16000 18000 20000 22000 Cluster K 갯수 카페 고객센터 챗봇 자동평가 사람평가 기존점수 1 1.5 2 2.5 3 3.5 4 0.2 0.22 0.24 0.26 0.28 0.3 0.32 0.34 0.36 0.38 0.4 90 92 94 96 98 100 102 104 106 Cluster K 갯수 XX 통운 고객센터 챗봇 자동평가 사람평가 기존점수 1 1.5 2 2.5 3 3.5 4 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 80000 90000 100000 110000 120000 130000 140000 Cluster K 갯수 클로바 한국어 대화 자동평가 사람평가 기존점수
  • 49.
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  • 51. unfold Table Lookup Table Lookup xi<latexit sha1_base64="L5Jbg0+FKUx4KVFoOIz15XfEJNA=">AAACHHicZVBNSwJRFH2jfZh9aS3bDInQSmYiqHaGi6I2RvkBKvLmedWHb94M790JZfAntK1dv6ZdtA36Nz11iMwDFw7n3Mu993ih4Bod59tKpdfWNzYzW9ntnd29/Vz+oK6DSDGosUAEqulRDYJLqCFHAc1QAfU9AQ1vVJn5jSdQmgfyESchdHw6kLzPGUUjPYy7vJsrOCVnDnuVuAkpkATVbt5Kt3sBi3yQyATVuuU6IXZiqpAzAdNsO9IQUjaiA2gZKqkPuhPPb53aRaP07H6gTEm05+rfiZj6Wk98z3T6FIf6vzcTf73i0irsX3RiLsMIQbLFpn4kbAzs2ed2jytgKCaGUKa4OdZmQ6ooQ5PP0poxVG7vzB8a0BCfcjm7Nb4KQwHXAQ45m2ZNbO7/kFZJ/bTkOiX3/qxQvkwCzJAjckxOiEvOSZnckCqpEUYG5Jm8kFfrzXq3PqzPRWvKSmYOyRKsrx8sQaB7</latexit><latexit 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sha1_base64="qXd0nsQcWREj5NaFiDsuNIpFW6A=">AAACGnicZVBNSwJRFH2jfZh9aS3bDInQSmYiqHaGi6I2CvkBKvLmedWHb94M792JZPAXtK1dv6ZdtG3Tv+mpQ6QeuHA4517uvccLBdfoOD9WKr2xubWd2cnu7u0fHObyRw0dRIpBnQUiUC2PahBcQh05CmiFCqjvCWh648rMbz6B0jyQjzgJoevToeQDzigaqdbs5QpOyZnDXiduQgokQbWXt9KdfsAiHyQyQbVuu06I3Zgq5EzANNuJNISUjekQ2oZK6oPuxvNLp3bRKH17EChTEu25+n8ipr7WE98znT7FkV71ZuKfV1xahYOrbsxlGCFIttg0iISNgT372+5zBQzFxBDKFDfH2mxEFWVo0lla8wyV+wfzhwY0xKdczm6Nb8JQwG2AI86mWRObuxrSOmmcl1yn5NYuCuXrJMAMOSGn5Iy45JKUyR2pkjphBMgLeSVv1rv1YX1aX4vWlJXMHJMlWN+/UgCffg==</latexit> Average ✓<latexit 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sha1_base64="hZLkpJDvmmUR8jVbSvY6z636d44=">AAACH3icZVBNSwJRFH2jfZh9aS3bDInQSmYiqHaGi6I2BvkBKvLmedWXb94M792JZPA/tK1dv6ZdtPXf9NQhUg9cOJxzL/fe44WCa3ScqZVKb2xubWd2srt7+weHufxRXQeRYlBjgQhU06MaBJdQQ44CmqEC6nsCGt6oMvMbL6A0D+QTjkPo+HQgeZ8zikaqt3EISLu5glNy5rDXiZuQAklQ7eatdLsXsMgHiUxQrVuuE2Inpgo5EzDJtiMNIWUjOoCWoZL6oDvx/NyJXTRKz+4HypREe67+n4ipr/XY90ynT3GoV72Z+OcVl1Zh/6oTcxlGCJItNvUjYWNgz563e1wBQzE2hDLFzbE2G1JFGZqIlta8QuX+wfyhAQ3xKZezW+ObMBRwG+CQs0nWxOauhrRO6ucl1ym5jxeF8nUSYIackFNyRlxyScrkjlRJjTDyTN7IO/mwPq0v69v6WbSmrGTmmCzBmv4CrAKhyw==</latexit><latexit sha1_base64="hZLkpJDvmmUR8jVbSvY6z636d44=">AAACH3icZVBNSwJRFH2jfZh9aS3bDInQSmYiqHaGi6I2BvkBKvLmedWXb94M792JZPA/tK1dv6ZdtPXf9NQhUg9cOJxzL/fe44WCa3ScqZVKb2xubWd2srt7+weHufxRXQeRYlBjgQhU06MaBJdQQ44CmqEC6nsCGt6oMvMbL6A0D+QTjkPo+HQgeZ8zikaqt3EISLu5glNy5rDXiZuQAklQ7eatdLsXsMgHiUxQrVuuE2Inpgo5EzDJtiMNIWUjOoCWoZL6oDvx/NyJXTRKz+4HypREe67+n4ipr/XY90ynT3GoV72Z+OcVl1Zh/6oTcxlGCJItNvUjYWNgz563e1wBQzE2hDLFzbE2G1JFGZqIlta8QuX+wfyhAQ3xKZezW+ObMBRwG+CQs0nWxOauhrRO6ucl1ym5jxeF8nUSYIackFNyRlxyScrkjlRJjTDyTN7IO/mwPq0v69v6WbSmrGTmmCzBmv4CrAKhyw==</latexit><latexit sha1_base64="hZLkpJDvmmUR8jVbSvY6z636d44=">AAACH3icZVBNSwJRFH2jfZh9aS3bDInQSmYiqHaGi6I2BvkBKvLmedWXb94M792JZPA/tK1dv6ZdtPXf9NQhUg9cOJxzL/fe44WCa3ScqZVKb2xubWd2srt7+weHufxRXQeRYlBjgQhU06MaBJdQQ44CmqEC6nsCGt6oMvMbL6A0D+QTjkPo+HQgeZ8zikaqt3EISLu5glNy5rDXiZuQAklQ7eatdLsXsMgHiUxQrVuuE2Inpgo5EzDJtiMNIWUjOoCWoZL6oDvx/NyJXTRKz+4HypREe67+n4ipr/XY90ynT3GoV72Z+OcVl1Zh/6oTcxlGCJItNvUjYWNgz563e1wBQzE2hDLFzbE2G1JFGZqIlta8QuX+wfyhAQ3xKZezW+ObMBRwG+CQs0nWxOauhrRO6ucl1ym5jxeF8nUSYIackFNyRlxyScrkjlRJjTDyTN7IO/mwPq0v69v6WbSmrGTmmCzBmv4CrAKhyw==</latexit> Softmax
  • 52.
  • 53. Type Default Value Type Default Value Max Epoch 100000 Dropout Input Keep Probability 0.8 Batch Size 1024 Dropout Intra Keep Probability 0.5 Unknown Count 3 Dropout Output Keep Probability 0.8 Target Perplexity 6.5 Dropout State Keep Probability 0.75 Threshold 0.963 Buckets [8, 13] Unk Ratio 0.333 Hidden Sizes [64, 64, 32] & [32, 64, 128] Learning Rate 0.0005 Bottleneck FALSE Unknown Percent 0.33 Bidirectional TRUE Max Gradient Norm 5 Min Vocab Count 2
  • 54.
  • 55. 0.00241 0.00373 0.00236 0.00977 0.00598 0.00325 0.01231 0.02275 0.00433 0.00562 0.00681 0.00646 0.00762 0.00821 0.00945 0.01089 0.00809 0.01020 0.01464
  • 56.
  • 57.
  • 58.
  • 59.
  • 60.
  • 61.
  • 62.
  • 63.
  • 64.
  • 65.
  • 66.
  • 67. ( ) (* 76 32 * ,( . , * 76 (, -0( * ( * , 76 32 ( 00* 0 * * 76 () . *. ( 76 32 -. (0 ) , 76 ) , ) .-0 * ) )) P , (* *( , C ), ( . ( , 76 5 1 9 : 8 4