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Conversational agents
This document discusses different types of conversational agents, including older chatbots like IBM Watson and modern chatbots using APIs. It also covers more advanced generative chatbots using LSTMs on large datasets as well as intelligent agents that can access information and perform actions. The document provides examples of how different agents work and considerations in building agents, from defining intents and slots to using neural networks, knowledge graphs, and APIs.
Conversational agents
- 1. Conversational Agents By SamWitteveen
- 2. About me • Icurrently spend most my time working on deep learning that supports algorithmic trading • Kids educational app company • Deep Learning/AI by far the most interesting thing worth working on
- 3. Conversational Agents • Oldstyle Chatbots - IBM, AIML, Eliza • Simple Modern Chatbot - Facebook bots, API.ai • Generative Chatbot - Twitter Bots • Intelligent Agent - Google Assistant, Kate
- 4. Considerations • Open domainvs. closed domain • Pre-made vs dynamic responses • Just chat vs trigger actions • Access to what information / Web
- 5. Old Style Chatbots:AIML • XML • Eliza / Alice bot • Pandorabots, Watson • Regex • Outdated and very fixed in the way that the work
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- 8. Simple Modern Chatbot •Intents and slots/variables “can i order a pizza” “can i order a hamburger” • intent = ‘order’ • item = ‘pizza/hamburger’ - slot variable
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- 10. Do it yourself •Simple Classification • Needs you to define classes first • Convert text in pipeline Logistic Regression Character CNNs RNNsMLPs
- 11. Pipeline • Tokenize • Vectorizewords • Embeddings • Word2vec / GloVE
- 12. Example Text Pipeline TensorFlow processing Tokenization getwords Speech Speech to Text Text to speech convert to embedding Beam Search Response Remove words (sometimes) convert to One-Hot Word2vec/GloVE Back to Words
- 13. Simple Chatbots • API.ai/ Google does these kinds of bots pretty simply • They only tend to work on a very closed domain • Good for simple tasks like ordering pizza, booking flights etc
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- 15. Generative Chatbot • Twitterbots made from speeches etc • LSTMs (char-RNN) or Seq2Seq • Large amount of training and data needed • Beam search on the response • Some token system for variables like names
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- 17. Generative Chatbot • Greatfor fun • Not ideal for business cases unless you have amazing data • Still a long way to go
- 18. Hybrid Agent • Speechto Text • Has the ability to access wider info • Custom NER, POS for extracting variables (deep learning ) • Graph system - for info and memory
- 19. Intent Ensemble • Ensemblesystem for classifying the input • LSTM Classification • Character CNN Classification • Bag of words
- 20. Intelligent Agent Response generation First Layerof Intent detection Actions Knowledge graph Speech STT TTS Check Layer of Intent detection Beam Search Response Entity Extraction Action Extraction Logic unstructured retrieval translation translation summarization
- 21. Knowledge bases • Getinfo for returning to user • Alternative: Freebase, DBpedia
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- 23. Translation text toquery • Similar to language translation • “How old is Taylor Swift?” • Entity: ’taylor swift’ , info.age
- 24. The really hardstuff • Stateful Conversations (G-Assistant) • Context • Detecting different users (G-Home)
- 25. Tips • Try tolimit the domain of conversation • Use Google Cloud for speech • Make use of off the shelf wherever possible • Use your own models for anything unique eg. NER, vocab etc. • Get as many example conversations as possible
- 26. Next Time • 1.Neural Machine Translation - Seq2Seq with attention • 2. CNNs for Text Class • 3. Advanced Embeddings • 4. Babl Q&A • 5. Advanced Summarization • 6. ParseySaurus - Google’s new NLP • 7. Hotdog / Not Hotdog
- 27. The End Contact email: s@maclea.ai twitter:sam_witteveen https://github.com/samwit/