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Talk to me – Chatbots und digitale Assistenten

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Menschliche Kommunikation folgt zwar einer ganzen Reihe von Regeln, diese lassen sich aber schwer formalisieren. Nicht zuletzt deshalb, weil in unseren Interaktionen immer auch eine Fülle von Welt- und implizitem Kontextwissen eine Rolle spielt. Rein regelbasierte Chatbots sind daher nicht nur äußert komplex in der Programmierung, sondern stoßen in vielen Anwendungsbereichen schnell an ihre Grenzen.

In diesem Vortrag gab Anna Weißhaar einen Überblick über die aktuellen Lösungen und Herausforderungen im Bereich digitale Assistenten. Der Fokus lag dabei auf Ansätzen, die Chatbots „chatty“ machen, sie also möglichst adäquat auf im Voraus unbekannte Nutzereingaben reagieren zu lassen.

Event: inovex Meetup: Das Unvorhersehbare vorhersagen: Zeitreihen und Chatbots, 26.03.2019
Speaker: Anna Weißhaar (inovex)

Mehr Tech-Vorträge: inovex.de/vortraege
Mehr Tech-Artikel: inovex.de/blog

Published in: Software
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Talk to me – Chatbots und digitale Assistenten

  1. 1. “Was mit Sprache” gelernt - Linguistik und Philosophie @unihd “Was mit Computern” gelernt - Informatik @hska Kombination - Search Engineer @inovex @findefain 2
  2. 2. A chatbot (also known as a smartbot, conversational bot, chatterbot, interactive agent, conversational interface, Conversational AI, or artificial conversational entity) is a computer program or an artificial intelligence which conducts a conversation via auditory or textual methods. Such programs are often designed to convincingly simulate how a human would behave as a conversational partner, thereby passing the Turing test. (Quelle: Internet, https://en.wikipedia.org/wiki/Chatbot) 3
  3. 3. Neither Aristotelian nor Russellian rules give the exact logic of any expression of ordinary language; for ordinary language has no exact logic. (P. F. Strawson, On Referring) 4
  4. 4. 5 1950 Turing Test 1966 Eliza 1972 Parry 1988 Jabberwacky 1992 Dr. Sbaitso 1995 Alice 2001 Smarterchild 2006 Watson (IBM) 2010 Siri 2012 Google Now 2015 Alexa, Cortana 2016 Tay https://chatbotsmagazine.com/a-visual-history-of-chatbots-8bf3b31dbfb2
  5. 5. 6 natürliches Interface Gespräche wie mit Menschen persönlich besserer Kundenservice
  6. 6. 7 unstrukturierte Eingabe Und dann würde mich halt auch noch interessieren, welche Größen es gibt. Die Sandalen “Mallorca” haben wir in den Größen 36 bis 47 vorrätig. natürlichsprachige Antwort Wie wird das Wetter am Samstag? Für Karlsruhe ist am 30.3. sonniges Wetter bei Temperaturen bis 16 Grad vorhergesagt. Interpretieren Absichten Kontextinformationen Anreichern "Weltwissen" bisheriger Kontext Reagieren ggf. Zustand aktualisieren
  7. 7. Eliza: Pattern-Matching + Templates 8 key: computer 50 decomp: * reasmb: Do computers worry you ? reasmb: Why do you mention computers ? reasmb: What do you think machines have to do with your problem ? reasmb: Don't you think computers can help people ? reasmb: What about machines worries you ? reasmb: What do you think about machines ? key: forget 5 decomp: * i forget * reasmb: Can you think of why you might forget (2) ? reasmb: Why can't you remember (2) ? reasmb: How often do you think of (2) ? reasmb: Does it bother you to forget that ? reasmb: Could it be a mental block ? reasmb: Are you generally forgetful ? reasmb: Do you think you are suppressing (2) ? decomp: * did you forget * reasmb: Why do you ask ? reasmb: Are you sure you told me ? reasmb: Would it bother you if I forgot (2) ? reasmb: Why should I recall (2) just now ? reasmb: goto what reasmb: Tell me more about (2). A.L.I.C.E.: AIML <category> <pattern> TELL ME ABOUT *</pattern> <template> Here is some information about <star/>: <url>https://en.wikipedia.org/wiki/<star/></url> </template> </category> <?xml version = "1.0" encoding = "UTF-8"?> <aiml version = "1.0.1" encoding = "UTF-8"?> <category> <pattern> HELLO ALICE </pattern> <template> Hello User </template> </category> </aiml>
  8. 8. 9
  9. 9. 10 “The Rasa Stack is a set of open source machine learning tools for developers to create contextual text- and voice-based chatbots and assistants.” https://rasa.com/ Versprechen: Weniger hartcodierte Regeln, mehr AI
  10. 10. 11 Interpretieren: Rasa NLU Reagieren: Rasa Core Input: Nutzereingabe Input: NLU-Output, Kontext Output: Intents, Entities Output: Nächste Aktion
  11. 11. entities => Dinge, über die gesprochen wird intents => Was will die Nutzerin? actions => Was kann der Bot tun? -------------- templates => Was kann der Bot sagen? slots => Gedächtnisinhalt 12
  12. 12. https://github.com/RasaHQ/starter-pack-rasa-stack 13 Hey there! Tell me your name. Hello! My name is Anna. Nice to meet you, Anna. How can I help? Tell me a joke, please! Chuck Norris can instantiate an abstract class. intents: - greet - name - joke entities: - name slots: name: type: text actions: - utter_greet - action_joke - utter_name templates: utter_greet: - text: Nice to you meet you {name}. How can I help? utter_name: - text: Hey there! Tell me your name.
  13. 13. 14 language: "en" pipeline: - name: "nlp_spacy" - name: "tokenizer_spacy" - name: "intent_featurizer_spacy" - name: "ner_crf" - name: "intent_classifier_sklearn" Tokenizer Featurizer NER Intents My name is Anna { "intent": { "name": "name", "confidence": 0.859506104253725 }, "entities": [ { "start": 11, "end": 15, "value": "anna", "entity": "name", "confidence": 0.9516187350992868, "extractor": "ner_crf" } ], "intent_ranking": [ { "name": "name", "confidence": 0.859506104253725 }, { "name": "greet", "confidence": 0.07173887988512613 }, ... ], "text": "My name is Anna" }
  14. 14. ## intent:greet - Hi - Hey - Hi bot - Hey bot ## intent:name - My name is [Juste](name) - I am [Josh](name) - I'm [Lucy](name) - People call me [Greg](name) 15 { "intent": "name", "entities": [ { "start": 15, "end": 19, "value": "Greg", "entity": "name" } ], "text": "People call me Greg" }
  15. 15. 1) Preprocessing - Tokenisierung: Satz => Token - Features erzeugen: Token => Embeddings 2) NER: Trainiere CRF - Features: Transformierte Trainingsdaten (berücksichtige Nachbarn) 3) Intents: Trainiere SVM - Features: Word-Embeddings (gemittelt) - Labels: Intent-Namen 16
  16. 16. 17 Tokenizer Featurizer NER Intents
  17. 17. { "intent": { "name": "name", "confidence": 0.9501842025598839 }, "entities": [ { "start": 13, "end": 17, "value": "anna", "entity": "name", "confidence": 0.9771150313654133, "extractor": "ner_crf" } ], "text": "They call me Anna" } 18
  18. 18. 19 https://rasa.com/docs/core/architecture/
  19. 19. 20 Intent Entities Slots Vergangenheit Policy Aktion
  20. 20. ## story_joke_02 * greet - utter_name * name{"name":"Anna"} - utter_greet * joke - action_joke * thanks - utter_thanks * goodbye - utter_goodbye 21 Hey there! Tell me your name. Hello! My name is Anna. Nice to meet you, Anna. How can I help? Tell me a joke, please! Chuck Norris can instantiate an abstract class. Thanks bot! My pleasure. Goodbye! Talk to you later!.
  21. 21. Stories => Vektordaten - generiere Graph - tracke Features: Intents, Entities, Slotwerte, letzte Aktion - erzeuge Featurevektoren: One-Hot oder BoW (Labelnamen) - berücksichtige Vergangenheit 22
  22. 22. Policies: - Memoization: - Exakter Match auf Trainingsdaten - Keras: Neuronales Netz - Default: LSTM - Recurrent Embedding Dialogue - Embeddings + Attention! => Grundidee: Typische Verläufe lernen, wichtige Informationen "merken" 23
  23. 23. 24
  24. 24. 25 https://blog.rasa.com/attention-dialogue-and-learning-reusable-patterns/
  25. 25. 26
  26. 26. + Weniger explizites Pattern-Matching + Verstehen: Semantische Ebene + Reagieren: "Gedächtnis" - Regelhunger => Datenhunger - Supervised Learning: gelabelte Daten - hohe Modellqualität nicht trivial ● Weltwissen/Kontextinformationen auch mit ML nicht gratis ● Gute Aktionen wollen implementiert werden 27
  27. 27. Anna Weißhaar Search Engineer inovex GmbH Ludwig-Erhard-Allee 6 76131 Karlsruhe anna.weisshaar@inovex.de 0173 3181 097

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