Three Key Engines of A Conversational Bot. Dr. Ming Zhou Principal Researcher Microsoft Research Asia Aug. 26, 2016

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1 Three Key Engines of A Conversational Bot Dr. Ming Zhou Principal Researcher Microsoft Research Asia Aug. 26, 2016 mingzhou@microsoft.com

2 Xiaoice, Rinna and Tay NL chat Single-turn and multi-turn Passive and proactive Speech enabled June, 2014 Weichat, Weibo, Win10 Imagebased chat Image search and recognition Face detection, beauty score and age detection Image commenting Aug Line and Twitter Skills Reminder, translation, mathematics Joking, role play Count sheep, fortune-telling, horoscope Late March 2016 Groupme, Kik, Twitter

3 General Architecture of Chatbot User Profiling Context Modeling Candidate Query Response Ranking Selection Understanding Generation Style Variation Intent, focus, topic, emotion, opinion Chit-chat, QnA and dialogue Session consistency Personalization Language style Dialect Emoticon General Topic Knowledge Base Data & Index Topic-Centered Knowledge Base

4 Three Key Engines TASK COMPLETION Task Completion Layer 3 Information and Answer Layer 2 Social Chat (chit-chat) Layer 1

5 Engine 1: Social Chat

6 Personalized Chat Sleep pattern Horoscope Interest

7 Deep-Chat Without Chat Knowledge With Chat Knowledge Do you know EXO? Do you know EXO? I do not want to see Kris in China because he has let EXO What? Right! Tell me something about EXO Because of Kris, I will no longer be a fan of EXO Tell what You were a fan of EXO? I am an audience in every concert of EXO Who do you like best in EXO? I like LAY best. Me too.

8 Image-Based Chat Duplicate Measured by local feature 10B Image index from BING Mona Lisa, Reproduction by Leonard of Mona Lisa Grandmother and Mona Lisa 这不是 Mona Lisa 吗? Isn t this Mona Lisa? Similar Measured by DNN feature 9M Image index from SNS Recognition Via DNN 10K Categories Mona Lisa

9 Image-Based Chat Duplicate Measured by local feature 10B Image index from BING 这小舌头 Similar Measured by DNN feature 9M Image index from SNS Look at the tiny tongue 这小舌头 Recognition Via DNN 10K Categories

10 Image-Based Chat Duplicate Measured by local feature 10B Image index from BING 看得我都流口水了 Similar Measured by DNN feature 9M Image index from SNS My mouth is watering Recognition Via DNN 10K Categories Food

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12 Engine 2: Information and Answer

13 Botification of Search Results Query processing Query type, query focus Today, Beijing is sunny, 64 degree. Bing Documents, answers and cards TechFest is Microsoft Research annual event Reinforcement learning is an area of machine learning Converting into NL expression Summarization, entity extraction, card to NL Leonardo DiCaprio

14 QnA Based on Multi-Intelligences Question Understanding Planner Knowledge Q/A Web Q/A Social Q/A MSRA.KS Ensemble Engine Answers with confidence and evidences

15 Engine 3: Task Completion

16 Dialogue System Enabling both card-based or chat-based Option-1 Trigger a form/url to complete the task Option-2 Trigger a dialogue to complete the task

17 Dialogue Process Hi there~ So how are u doing? I m fine. Please reserve a trip to Seattle for me. Do you have any preferred hotel? Hilton, please. I will check-in on , and stay there 3 days Which type of room do you like? Single room is just ok to me. Got it, I will recommend you some travel packages now. Intent=Nil Intent=Book Travel Package; Destination=Seattle; Hotel=Hilton; Check-in Date= ; Length of Stay=3 Days; Room Type=Single Room Query Understanding Intent detection Slot value extraction Chit-chat Response Dialogue Response Dialogue Response Dialogue Response Response Generation Call services Call chit-chat Slot Name Intent Destination Hotel Check-in Date Length of Stay Room Type Slot Value Book Travel Package Seattle Hilton Days Single Room Dialogue Management Intent changes leads to state transition Slot info update Select next slot to ask

18 Applications Shopping Guider Robot Brain Customer Service

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22 Robot Brain

23 Customer Service

24 Customer Service (HI) Unsatisfied Satisfied

25 Research Opportunities Productivity of knowledge engineering Learning from existing data to build hierarchical knowledge base Powerful tool for human editing and updating Context-sensitive understanding and responding Better context modelling about current topic and task Allow various styles of user s input to map into intent space Semantic computing (distance, entailment) Embedding of word, entity, sentence Lexical semantic relation (synonym, hyponym, hypernym, distance)

26 Thanks!