Conversational Systems in the Era of Deep Learning and Big Data. Ian Lane Carnegie Mellon University
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2 Conversational Systems in the Era of Deep Learning and Big Data Ian Lane Carnegie Mellon University
3 End-to-End Trainable Neural Network Models for Task Oriented Dialog Ian Lane Carnegie Mellon University User Agent 3
4 Trainable End-to-End Models for Dialog Can we learn to perform task-oriented dialog by modeling human conversations? User Agent 4
5 Trainable End-to-End Models for Dialog Can we learn to perform task-oriented dialog by modeling human conversations? User I m looking for an Italian Restaurant for Dinner. Agent 5
6 Trainable End-to-End Models for Dialog Can we learn to perform task-oriented dialog by modeling human conversations? User I m looking for an Italian Restaurant for Dinner. Sure I can help you with that. Whereabouts are you thinking? Agent 6
7 Trainable End-to-End Models for Dialog Can we learn to perform task-oriented dialog by modeling human conversations? User I m looking for an Italian Restaurant for Dinner. Somewhere in Menlo Park or Palo Alto. Sure I can help you with that. Whereabouts are you thinking? Agent 7
8 Trainable End-to-End Models for Dialog Can we learn to perform task-oriented dialog by modeling human conversations? User I m looking for an Italian Restaurant for Dinner. Somewhere in Menlo Park or Palo Alto. Sure I can help you with that. Whereabouts are you thinking? Well there is a couple of good places Agent 8
9 Trainable End-to-End Models for Dialog Can we learn to perform task-oriented dialog by modeling human conversations? User I m looking for an Italian Restaurant for Dinner. Agent Model Somewhere in Menlo Park or Palo Alto. Output 1 Output 2 Agent Knowledge base 9
10 End-to-End Models for Dialog 1 0
11 End-to-End Models for Dialog 1 1
12 1 2 Trainable Task-Oriented Models for Dialog Agent Understand user s input in context Take Action to take given user input, dialog and task history Respond to user? Perform API call / database look up? Perform other action? Or combination of above. Update dialog state User Estimate of user goal / goal change
13 1 3 An Neural Network Model for Dialog Agents Agent Model Perform de-lexicalization on input (i.e. Named Entity Recognition) Update dialog state (via. LSTM model) Estimate beliefs state Action: Issue API Call and process results (if required) Action: Update reference to results (if required) Action: Response generation Estimate de-lexicalized response that best matches current state from all possible responses
14 End-to-End Neural Network Models for Dialog 1 4
15 Training - Approach Manual Annotation of Training Data Not Required Assumes NLU for delexicalization and named entity detection Train on dialogs from human-to-human chat interaction Assumes turn-based interaction can be extend to also model timing Supervized Training Treat as a classification task per turn Select best output given history Reinforcement Learning Optimize based on end-of-dialog reward 15
16 Training Results and Challenges Prediction Accuracy Issues Even if action taken by agent at specific turn does not match training training data it may still be appropriate training data is fixed, User does not respond based on output of Model Can we simulate complete USER-AGENT interactions to improve dialog? 16
17 Joint Modeling of Users and Agents User Goal User I m looking for an Italian Restaurant for Dinner. Somewhere in Menlo Park or Palo Alto. Sure I can help you with that. Whereabouts are you thinking? Well there is a couple of good places Agent 17
18 Joint Modeling of Users and Agents Can we jointly model task-oriented dialog to simulate human conversations? User Model Agent Model I m looking for an Italian Restaurant. User Goal USER Somewhere in Menlo Park or Palo Alto. Sure I can help you with that. Whereabouts are you thinking? Well there is a couple of good places AGENT Knowledge base 18
19 Joint Modeling of Users and Agents User Model Agent Model 19
20 Joint Modeling of Users and Agents User Model Agent Model 20
21 Joint Modeling of Users and Agents User Model Agent Model 21
22 Joint Modeling of Users and Agents 22
23 Results (DSTC 2 Task) Dialog Success Rate Significantly higher dialog success rate using jointly optimized models (65%) than optimizing AGENT only (50%), or baseline trained via supervised learning (35%) 23
24 24
25 Conclusions and Challenges Training task oriented dialog systems directly from human conversations seems plausible End-To-End trainable give logs of dialog + API calls Well suited for task-oriented dialogs that include social, task-orientated dialog and external knowledge base look-up or actions i.e. Product and service support (Call-Centers / Messaging Interfaces) Ability to model both Agent and User in a conversation With data can model personas or individual Agents or Users Exploring boot-strapping dialog systems on-the-fly Generate suggested response see if Agent follows suggestion or not Update models for next utterance / dialog 25
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