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[211] 인공지능이 인공지능 챗봇을 만든다

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[211] 인공지능이 인공지능 챗봇을 만든다

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[211] 인공지능이 인공지능 챗봇을 만든다

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  3. 3. 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
  4. 4. 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
  5. 5. 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
  6. 6. 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
  7. 7. Data Input Data Output
  8. 8. 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
  9. 9. unfold Table Lookup Table Lookup xi<latexit sha1_base64="L5Jbg0+FKUx4KVFoOIz15XfEJNA=">AAACHHicZVBNSwJRFH2jfZh9aS3bDInQSmYiqHaGi6I2RvkBKvLmedWHb94M790JZfAntK1dv6ZdtA36Nz11iMwDFw7n3Mu993ih4Bod59tKpdfWNzYzW9ntnd29/Vz+oK6DSDGosUAEqulRDYJLqCFHAc1QAfU9AQ1vVJn5jSdQmgfyESchdHw6kLzPGUUjPYy7vJsrOCVnDnuVuAkpkATVbt5Kt3sBi3yQyATVuuU6IXZiqpAzAdNsO9IQUjaiA2gZKqkPuhPPb53aRaP07H6gTEm05+rfiZj6Wk98z3T6FIf6vzcTf73i0irsX3RiLsMIQbLFpn4kbAzs2ed2jytgKCaGUKa4OdZmQ6ooQ5PP0poxVG7vzB8a0BCfcjm7Nb4KQwHXAQ45m2ZNbO7/kFZJ/bTkOiX3/qxQvkwCzJAjckxOiEvOSZnckCqpEUYG5Jm8kFfrzXq3PqzPRWvKSmYOyRKsrx8sQaB7</latexit><latexit 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  10. 10. 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
  11. 11. 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
  12. 12. ( ) (* 76 32 * ,( . , * 76 (, -0( * ( * , 76 32 ( 00* 0 * * 76 () . *. ( 76 32 -. (0 ) , 76 ) , ) .-0 * ) )) P , (* *( , C ), ( . ( , 76 5 1 9 : 8 4
  13. 13. )( - (

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