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Introduction to Dialogue Systems Introduction to Dialogue Systems

Personal Assistants are becoming a realityPersonal Assistants are becoming a reality

Dr Natalia KonstantinovaUniversity of Wolverhampton

11 April 2014

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OutlineOutline

• What is a dialogue system?• System structure and classification;• Evaluation;• Examples of existing systems;• Future directions;• IQA;

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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.

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Optimistic viewOptimistic view

• Hollywood and Artificial Intelligence (robots that can think and act like humans)

• Video

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Realistic viewRealistic view

• Talk to Alan (or to some other HAL personalities)

• Chat with ALICE • Three bots talking

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What is a dialogue system?What is a dialogue system?

Ideas?• • • •

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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.”

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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

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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)

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““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

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Embodied conversational agentsEmbodied conversational agents

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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.

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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

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NLUNLU

• Aim of NLU module:• produce a semantic representation

appropriate for a dialogue task.

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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)

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Dialogue managerDialogue manager

• Interlink of NLU and NLG

• Responsible for the content generation • (taking decisions about what to say and how)

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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

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Speech synthesisSpeech synthesis

• Is optional• Uses output on NLG module to generate

natural speech

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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.

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Finite-state DMFinite-state DM• A set of states• System totally controls the conversation

with the user

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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

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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.

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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 andintentions) model.

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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

Example of architectureExample of architecture

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EvaluationEvaluation

• How to make an objective evaluation?• Task-based evaluation (Dale, Moisi, and

Somers, 2000):• task completion success;• efficiency cost;• quality costs.

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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

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DifficultiesDifficulties

• Necessity to collect training corpus:• Wizard-of-Oz experiments • Prompting experiments

• Error handling

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ChatbotsChatbots• http://www.chatbots.org/

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;

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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;

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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.