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idalab seminar #3 machine learning for dialogue alan nichol

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idalab seminar #3 machine learning for dialogue alan nichol

Conversational software can be built using several approaches: from hand-coded (flow charts) to completely data driven (seq2seq models). Alan Nichol shared with us his insight on the state of the art of chatbots.

Right now, there are promising building blocks, but very little understanding of how to put them together to make really engaging experiences. And these are just the rudiments of conversation: questions and answers, interwoven threads, knowing when to remember and when to forget.

Conversational software can be built using several approaches: from hand-coded (flow charts) to completely data driven (seq2seq models). Alan Nichol shared with us his insight on the state of the art of chatbots.

Right now, there are promising building blocks, but very little understanding of how to put them together to make really engaging experiences. And these are just the rudiments of conversation: questions and answers, interwoven threads, knowing when to remember and when to forget.

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idalab seminar #3 machine learning for dialogue alan nichol

  1. 1. Alan Nichol Agency for Data Science Machine learning & AI Mathematical modelling Data strategy Machine Learning for Dialogue idalab seminar #3 | October 2016
  2. 2. Machine Learning for Dialogue Alan Nichol Idalab seminar, Oct 2016
  3. 3. ● What do chat systems need to do ● Hand-coded vs data-driven approaches ● Abstracting Language ● Latent Variables ● End-to-End models ● Reinforcement Learning Outline
  4. 4. Recipe Search parse: inform(cuisine=french) action: query DB action : suggest parse: ask(diet=vegan) action : query DB look up previous suggestion utter a suggestion utter an answer
  5. 5. Hand-crafted vs Data-driven hand-crafted data-driven
  6. 6. Abstracting Language: Dialogue Acts dialogue act entities policy belief state action_1 action_2 action_N inform(cuisine=x) “I would like to eat some French food” “Mexican Please” ask_constraint(key=x,val=y) “Is that vegetarian?” “Does that take a long time to make?”
  7. 7. Seq2seq models and Latent Variables
  8. 8. Components
  9. 9. golastmile.com Key References 1. A Network-based End-to-End Trainable Task-oriented Dialogue System. Wen, Gasic et al 2. Machine Comprehension using match-LSTM and answer pointer. Wang and Jiang. 3. A Neural Conversational Model. Vinyals and Le. 4. Ask Me Anything: Dynamic Memory Networks for Natural Language Processing, Kumar et al. 5. Reinforcement Learning Neural Turing Machines - Revised. Zaremba and Sustkever.
  10. 10. Stay in touch! Alan Nichol CTO alan@golastmile.com

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