CISC882: Natural Language Processing Final Project A Tale of Two Dialogue Models By Sachin Kamboj
The Two Models • Missing Axiom Theory Model • Circuit Fix-It • Ronnie W. Smith, D. Richard Hipp, Alan W. Biermann: An architecture for voice dialog systems based on prolog style theorem proving, Computational Linguistics, Volume 21, Number 3, September 1995. • Multimodal Speech and Gesture Models • Oviatt et. al.: Designing the user interface for multimodal speech and pen-based gesture applications: State of the art systems and future research directions, Human Computer Interaction, 2000, vol. 15(4) pg 263-322
Circuit Fix-ItAims/Target Behavior • The system should be able to handle: • Convergence to a goal • Subdialogues and effective movement between them • An accounting of user knowledge and abilities • Change of initiative • Expectation of user input
Circuit Fix-ItSample Dialogs Computer: Put the knob to one zero. User: Okay. Computer: What is the voltage between connector one two one and connector three four? User: I do not know. Computer: Locate the voltmeter. User: Done. Computer: Add a wire between the minus com hole on the voltmeter and the connector one two one. User: How do I do that? Computer: Connect the end of the black wire with the small plug to the minus corn hole on the voltmeter. User: Done. Computer: Connect the end of the black wire with the large plug to connector one two one. User: Which Knob? Computer: Near the top boundary and in the center is a green region. Now, in the middle right of this green region is the control knob. User: Okay. Computer: Put the knob to one zero.
Circuit Fix-ItProlog Style Theorem Proving • Based on the Missing Axiom Theory • System is built around a theorem prover and the role of language is to supply the missing axioms • Goal is stated as a prolog axiom to be proven • The system tries to prove the axiom in a top-down fashion. • If the proof succeeds using internally available knowledge, the dialog terminates without any interaction with the user. • If the proof fails the system tries to find the missing axiom by engaging in a dialog observeposition(sw1,X) ← find(sw1), reportposition (sw1,X)
Circuit Fix-ItImplementing the Subdialog Feature • One of the requirements of the system is to allow subdialogs. • As the system engages in conversation, only to prove missing axioms, each subdialog involves a separate proof. • Hence the system cannot follow a simple depth-first policy to complete a proof. • Instead, to switch between subdialogs, the system should allow the freezing of any proof and the transfer of control to a different proof • Partially completed proofs have to be maintained in memory. • Freezing of proofs handled through an Interruptible Prolog Simulator (IPSIM)
Circuit Fix-ItAccounting for User Knowledge • The system should know what the user is capable of doing • The requests should match the abilities of the user • Abilities of different users will vary • Novice users will know how to adjust a knob but may not know how to take a voltmeter reading • The system uses a user model to determine what can be expected of the user. • The user’s capabilities are specified in the form of prolog style rules • If the input describes some physical state, then conclude that the user knows how to observe the physical state. In addition if the physical state is a property, then infer that the user knows how to locate the object that has that property.
Circuit Fix-ItMechanisms for Obtaining Variable Initiative • Variable initiative takes a role in selecting the next subdialog to be entered. • The system implements four levels of initiative: • Directive Mode: unless the user needs clarification, the system selects its response according to the next goal • Suggestive Mode: the system will select its response depending on the next goal but will allow interruptions to subdialogs about related goals • Declarative Mode: the user has dialog control, but the system is free to mention relevant facts • Passive Mode: The user has complete control. The system will provide information only in direct response to the user’s questions.
Circuit Fix-ItImplementation and Uses of Expectation • If the computer produces an utterance that is an attempt to have a specific task step S performed, there are expectations for any of the following types of responses: • A statement about the missing or uncertain background knowledge necessary for the accomplishment of S • A statement about a subgoal of S. • A statement about the underlying purpose for S. • A statement about ancestor task steps of which accomplishment of S is a part • A statement indicating the accomplishment of S. • Expectations serve two purposes: • The detection and correction of errors • Provide an indication of the shift between subdialogs
Circuit Fix-ItImplementation and Uses of Expectation • The system computes the expectations and the cost of each expectation. • The system also computes a set of “meanings” (or semantic representations) of user utterances with a corresponding cost • The system combines the two costs: C = βμ + (1 – β)E • The meaning with the smallest total cost is selected as the output of the parser
Circuit Fix-ItImplementation and Uses of Expectation • An important side effect of matching meanings with expectations is the ability to interpret an utterance whose content does not specify its meaning. • The reference of pronouns • Turn the switch up • Where is it? • The meaning of short answers • Turn the switch up • Okay • Maintaining dialog coherence
Basic Algorithm ZmodSubdialog(Goal) Create subdialog data structures While there are rules available which may achieve Goal Grab next available rule R from knowledge; unify with Goal If R trivially satisfies Goal, return with success If R is vocalize(X) then Execute verbal output X (mode) Record expectation Receive response (mode) Record implicit and explicit meanings for response Transfer control depending on which expected response was received Success response: Return with success Negative response: No action Confused response: Modify rule for clarification; prioritize for execution Interrupt: Match response to expected response of another subdialog; Go to that subdialog (mode) If R is a general rule then Store its antecedents While there are more antecedents to process Grab the next one and enter ZmodSubdialog with it If the ZmodSubdialog exits with failure then terminate processing of R If all antecedents of R succeed, return with success Halt with failure
Introduction • What are multimodal interfaces? • Humans perceive the world through senses • Ears (hearing), Eyes (sight), Nose (smell), Skin (touch) and Tongue (taste) • Communication through one sense is known as a mode • Computers may process information through modes as well • Keyboards, Microphone, Mice, etc. • Multimodal interfaces try to combine two different modes of communicating. • Slide borrowed from a talk on ‘Multimodal Interfaces’ by Joe Caloza
Advantages • Combination of modalities allows more powerfully expressive and transparent information seeking dialogues: • Different modalities provide complimentary capabilities • Users prefer speech input for functions like describing objects and events and for issuing commands • Pen input is preferred for conveying symbols, signs and gestures and for pointing and selecting visible objects • Multimodal pen/voice interaction can result in 10% faster task completion time, 36% fewer task-critical content errors 50% fewer spontaneous disfluencies and shorter and more simplified linguistic constructions • Corresponds to a 90-100% user preference to interact multimodally
Advantages (2) • Able to support superior error-handling compared with unimodal recognition interfaces • User-centric reasons • Users will select the input mode that they judge to be less error prone for a particular lexical context • Users’ language is simplified when interacting multimodally • Users have a strong tendency to switch modes after system errors • System-centric reasons • A well-designed multimodal architecture can support mutual disambiguation of input signals
Advantages (3) • Allow users to exercise selection and control over how they interact with the computer • Hence can accommodate a broader range of users • A visually impaired user may prefer speech input and TTS output • A user with a hearing impairment, strong accent, or a cold may prefer pen input • Multimodal interfaces are particularly suitable for supporting mobile tasks such as communication and personal navigation
Types of Multimodal Architecture • Can be subdivided into two main types: • Early Fusion • Integrate signals at the feature level • Based on Hidden Markov Models and Temporal Neural Networks • The recognition process in one mode influences the course of recognition in the other • Used for closely coupled and synchronized modalities (eg speech and lip movement) • Systems tend not to apply or generalize as well if the modes differ substantially in the information content or time scale characteristics • Require a large amount of training data to build the system.
Types of Multimodal Architecture • Late Fusion • Integrate information at a semantic level • Use individual recognizers trained using unimodal data • Systems based on semantic fusion can be scaled up easier whether in the number of input modes or the size and type of the vocabulary sets • Require an architecture that supports fine-grained time stamping of at least the beginning and end of each input signal • Required to figure out if two signals are part of a multimodal construction or whether they should be interpreted as unimodal commands.
Multimodal Architecture Speech Pen, Glove, Laser Gesture Recognition Speech Recognition Gesture Understanding Context Management NLP Multimodal Integration Dialogue Manager Graphics VR TTS Application Invocation and Coordination Response Planning App1 App2 App3
Applications • OGI QuickSet System • Enables a user to create and position entities on a map with speech, pen-based gestures and direct manipulation. • These entities are then used to initialize/run a simulation • IBM’s Human-Centric Word Processor • Combines Natural Language understanding with pen-based pointing and selection gestures. • Used to correct, manipulate and format text after it has been entered • Boeing’s Virtual Reality Aircraft Maintenance Training Prototype • Used for accessing the maintainability of new aircraft designs and training mechanics in maintenance procedures using VR
Applications • Meditor: Multimode Text Editor • Combines keyboard, Braille terminal, a French text-to-speech synthesiser, and a speech recognition system. • Allows Blind people to perform simple Document editing tasks. • MATCH • Multimodal Access to City Help • A Multimode Portable Device that accepts speech and pen gestures created by ATT&T • Allows mobile users to access restaurant and subway information for New York City
Conclusion • Multimodal systems are useful for a wide variety of applications • They provide increased robustness, ease of use and flexibility. • They provide accessibility to computer to a wider and more diverse range of users • However the area still needs a lot of research and a lot of challenges need to be overcome
References • Harald Aust et al.: The Philips Automatic Train Timetable Information System, Speech Communication 17 (1995) 249-262 • Gavin E. Churcher, Eric S Atwell, Clive Souter: Dialogue Management Systems: A Survey and Overview University of Leeds, Research Report Series, Report 97.06, Feb 1997 • Sharon J. Goldwater et al.: Building a Robust Dialogue System with Limited Data, ANLP-NAACL 2000 Workshop: Conversational Systems • Staffan Larsson et al.: GoDiS- An Accommodating Dialogue System, ANLP-NAACL 2000 Workshop: Conversational Systems • Diane J. Litman and Shimei Pan: Designing and Evaluating an Adaptive Spoken Dialogue System, User Modelling and User-Adapted Interaction 12: 111-137, 2002 • Michael F. McTear: Spoken Dialogue Technology: Enabling the conversational user interface, ACM Computing Surveys, Vol 34, No. 1, March 2002, pp. 90-169 • Mikio Nakano et al.: WIT: A toolkit for building robust and real-time spoken dialogue systems, 1st Sigdial Workshop at ACL2000 • Stephanie Seneff and Joseph Polifroni: Dialogue Management in the Mercury Flight Reservation System, ANLP-NAACL 2000 Workshop: Conversational Systems, May 2000, pp 11-16 • Satinder Singh et al.: Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun SystemJournal of Artificial Intelligence Research 16 (2002) 105-133 • M. A. Walker et al.: Evaluating spoken dialogue agents with PARADISE: Two case studies, Computer Speech and Language (1998) 12, 317-347 • Wayne Ward and Bryan Pellom: The CU Communicator System, International Workshop on Automatic Speech Recognition and Understanding (1999), Section 5 • Sandra Williams: Dialogue Management in Mixed-initiative, Cooperative, Spoken Language System11th Twente Workshop on Language Technology (TWLT11) Dialogue Management in Natural Language Systems, Enschade, Netherlands, June 1996