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Dialogue systems and personal assistants

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This presentation covers dialogue systems: their definition, basic structure (covering all modules: natural language understanding, dialogue manager, natural language generation), evaluation and the way they can be used. We also provide details about future directions and discusses current personal assistants: SIRI, S-Voice, Cortana, Maluuba etc.

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Dialogue systems and personal assistants

  1. 1. 1 Introduction to Dialogue SystemsIntroduction to Dialogue Systems Personal Assistants are becoming a realityPersonal Assistants are becoming a reality Dr Natalia Konstantinova University of Wolverhampton 11 April 2014
  2. 2. 2 OutlineOutline • What is a dialogue system? • System structure and classification; • Evaluation; • Examples of existing systems; • Future directions; • IQA;
  3. 3. 3 DefinitionDefinition • Artificial intelligence – idea to teach machines to think and act as humans. • NLP – give machines the ability to read, understand and use natural language. • Dialogue systems – part of artificial intelligence challenge.
  4. 4. 4 Optimistic viewOptimistic view • Hollywood and Artificial Intelligence (robots that can think and act like humans) • Video
  5. 5. 5 Realistic viewRealistic view • Talk to Alan (or to some other HAL personalities) • Chat with ALICE • Three bots talking
  6. 6. 6 What is a dialogue system?What is a dialogue system? Ideas? • • • •
  7. 7. 7 DefinitionDefinition • Editors of the Journal of Dialogue Systems : “A dialogue system is a computational device or agent that • (a) engages in interaction with other human and/or computer participant(s); • (b) uses human language in some form such as speech, text, or sign; • and (c) typically engages in such interaction across multiple turns or sentences.”
  8. 8. 8 Other termsOther terms • Conversational agents (Jurafsky and H.Martin, 2006), (Lester, Branting, and Mott, 2004) • “Chatterbot” or “chatbot”, first coined by Mauldin (1994): • simple dialogue systems, primarily based on simple analysis of keywords in the input and usage of different templates
  9. 9. 9 Where are they used?Where are they used? Usually embedded in such applications as: • customer service, • help desks, • website navigation, • guided selling, • technical support (Lester, Branting, and Mott, 2004)
  10. 10. 10 ““Body” for a dialogue systemBody” for a dialogue system • Embodied conversational agents (Cassell et al., 2000): • has a “body”, where both verbal and nonverbal devices advance and regulate the dialogue between the user and the computer. • Financial advisers, sales agents at online shops
  11. 11. 11 Embodied conversational agentsEmbodied conversational agents
  12. 12. 12 System structureSystem structure • They generally consist of 5 main components (Jurafsky and H.Martin, 2006): 1. speech recognition; 2. natural language understanding (NLU); 3. dialogue management; 4. natural language generation (NLG); 5. speech synthesis.
  13. 13. 13 System structureSystem structure • Some modules are optional: • e.g. speech recognition and speech synthesis • Dialogue systems involving speech are more complicated: • need to deal with errors in speech recognition • Speech recognition can be dialogue-state dependant
  14. 14. 14 NLUNLU • Aim of NLU module: • produce a semantic representation appropriate for a dialogue task.
  15. 15. 15 Dialogue managerDialogue manager • One of the most important parts of DS (Dale, Moisi, and Somers, 2000): • interpret the speech acts; • carry out problem-solving actions; • formulate response; • in general maintain the system's idea of the state of the discourse (e.g. dialogue move tree)
  16. 16. 16 Dialogue managerDialogue manager • Interlink of NLU and NLG • Responsible for the content generation • (taking decisions about what to say and how)
  17. 17. 18 NLGNLG • Chooses syntactic structures and words to express the intended meaning, which was formulated by a dialogue manager. • How?: • Templates to generate “prompts” (generated outputs) • Advanced natural language generators
  18. 18. 19 Speech synthesisSpeech synthesis • Is optional • Uses output on NLG module to generate natural speech
  19. 19. 20 System classificationSystem classification • Jurafsky and H.Martin (2006): 4 main types of dialogue management (DM) architectures: 1. finite-state DM; 2. frame/form based DM; 3. information-state DM; 4. plan-based DM.
  20. 20. 21 Finite-state DMFinite-state DM • A set of states • System totally controls the conversation with the user
  21. 21. 22 Frame/form based DMFrame/form based DM • Simple and the most widely used • Asks questions to fill in the slots in the frame • Perform a database query • E.g. booking a holiday
  22. 22. 23 Information-state DMInformation-state DM • More complicated • Incorporates several ways to achieve a result. • Components: • the information state (the “discourse context” or “mental model”); • dialogue act interpreter (or “interpretation engine”); • dialogue act generator (or “generation engine”); • set of update rules (to update information state); • control structure to select needed update rule.
  23. 23. 24 Plan-based DMPlan-based DM • The most sophisticated one • It interprets conversation as creation of a plan and then interprets a plan “in reverse” • Is often referred as BDI (belief, desire and intentions) model.
  24. 24. 25 Other classificationsOther classifications • system-initiative (or single initiative systems)  mixed initiative systems • spoken dialogue systems  text dialogue systems • multi-modal dialogue systems  unimodal dialogue systems • domain restricted dialogue systems  Open domain dialogue systems
  25. 25. Example of architectureExample of architecture 26
  26. 26. 27 EvaluationEvaluation • How to make an objective evaluation? • Task-based evaluation (Dale, Moisi, and Somers, 2000): • task completion success; • efficiency cost; • quality costs.
  27. 27. 28 EvaluationEvaluation • Asking people to complete a question list and rank the quality of the system giving grades: • E.g. evaluate naturalness • Maybe not very objective
  28. 28. 29 DifficultiesDifficulties • Necessity to collect training corpus: • Wizard-of-Oz experiments • Prompting experiments • Error handling
  29. 29. 30 ChatbotsChatbots • http://www.chatbots.org/
  30. 30. Competitors of SIRICompetitors of SIRI • Cortana by Microsoft; • Voice Mate by LG; • S-Voice by Samsung; • Google Now; • E.g. Android versions: Maluuba; Robin; Iris; Vlingo; Skyvi; • More similar apps; 31
  31. 31. Further directionsFurther directions • Currently DM in all commercial systems is rule- based; • What can be used? • Reinforcement learning (hierarchical RL); • Online learning; • Dialogue manager based on partially observable Markov decision process (POMDP); • Quality-adaptive DM; 32
  32. 32. 33 ReferencesReferences • Cassell, Justine, Joe Sullivan, Scott Prevost, and Elizabeth F. Churchill, editors. 2000. Embodied Conversational Agents. Cambridge, MA: MIT Press. • Dale, Robert, Hermann Moisi, and Harold Somers, editors. 2000. Handbook of Natural Language Processing. Marcel Dekker, Inc. • Jurafsky, Daniel and James H.Martin. 2006. Speech and language processing an introduction to natural language processing, computational linguistics, and speech recognition. Prentice-Hall, Inc. • Lester, James, Karl Branting, and Bradford Mott. 2004. Conversational agents. In Munindar P Singh, editor, The Practical Handbook of Internet Computing. Chapman & Hall. • Mauldin, Michael L. 1994. Chatterbots, Tinymuds, and the Turing test: Entering the Loebner prize competition. In Proceedings of the Eleventh National Conference on Artificial Intelligence. AAAI Press. • Mitkov, Ruslan, editor. 2003. Handbook of Computational linguistics. Oxford University Press, USA. • Sacks, H., E. A. Schegloff, and G. Jefferson. 1974. A simplest systematics for the organization of turn-taking for conversation. Language, 50(4):696-735. • Varges, S., F. Weng, and H. Pon-Barry. 2007. Interactive question answering and constraint relaxation in spoken dialogue systems. Natural Language Engineering, 15(1):9-30. • Webb, Nick and Bonnie Webber. 2009. Special issue on interactive question answering: Introduction. Natural Language Engineering, 15(1):1-8, January.

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