Diane J. Litman

CS LRDC
Department of Computer Science Learning Research & Development Ctr.
5105 Sennott Square 741 LRDC
210 South Bouquet Street 3939 O'Hara Street
University of Pittsburgh University of Pittsburgh
Pittsburgh, PA 15260 Pittsburgh, PA 15260
(phone) 412-624-8838 (phone) 412-624-1261
(fax) 412-624-8854 (fax) 412-624-9149

Faculty for the graduate Intelligent Systems Program
Member of the Pittsburgh Science of Learning Center
Co-Director nlp@pitt and Director ITSPOKE

Research

My research is in the area of artificial intelligence, and includes contributions in the areas of computational linguistics, knowledge representation and reasoning, natural language learning, plan recognition, spoken language, user modeling, and artificial intelligence and education. My work has included both fundamental research, and applied research resulting in technology transfer and patents.

My most recent research has been in the area of spoken dialogue systems, systems in which human users speak to a computer in order to achieve their goals. The potential benefits of such systems include remote or hands-free access to a variety of information services, ease of use, and naturalness. Spoken dialogue systems are among the few realized examples of open-ended, real-time, goal-oriented interaction between humans and computers, and are therefore an exciting testbed for artificial intelligence research. At AT&T, my research focused on the use of machine learning techniques for optimizing and adapting dialogue system behavior, the use of user intonation in spoken utterances to detect and repair system errors, and performance evaluation. At the University of Pittsburgh, I am continuing these lines of research in the context of ITSPOKE (an Intelligent Tutoring SPOKEn dialogue system), and am in addition focusing on how to enhance the effectiveness of text-based tutorial dialogue systems through the use of spoken language processing and affective computing. The Natural Language Processing Lab homepage contains further details about my research and that of my colleagues.

In the areas of knowledge representation and plan recognition, my research has emphasized the design and implementation of plan-based knowledge representation systems, the application of plan recognition to natural language and graphical intelligent interfaces, and the development of a rule-based extension of C++ with an application to alarm monitoring in a telephone switching system.

Selected Publications (by year)

  • 2003-present

    News

  • Aug. 2007 - July 2008: Leverhulme Visiting Professor, University of Edinburgh (Informatics). Here is my UK Leverhulme Lecture series.
  • April 2008: Invited Speaker, Affective Language in Human and Machine Symposium of the AISB Convention
  • February 2008: Distinguished Lecturer, School of Informatics, University of Edinburgh.
  • June 2007: U. of Pittsburgh Exhibitor, Coalition for National Science Funding, Capitol Hill.
  • April 2007: Best Paper Award (Late-Breaking News) at HLT-NAACL 2007 for Exploring Affect-Context Dependencies for Adaptive System Development by Forbes-Riley, Rotaru, Litman, and Tetreault
  • September 2006: People's Choice Best Paper Award for Discourse Structure and Speech Recognition Problems (by Mihai Rotaru and Diane Litman), Ninth International Conference on Spoken Language Processing (Interspeech/ICSLP), 2006
  • July 2006: Invited Speaker at 7th SIGdial Workshop on Discourse and Dialogue
  • June 2006: Keynote Speaker at Human Language Technology Conference/North American chapter of the Association for Computational Linguistics Annual Meeting (ppt)
  • September 2005: Speech Communications Best Paper Award 2003-2004 was given by the European Association for Signal Processing (Eurasip) for "Prosodic and other cues to speech recognition failures" by Hirschberg, Litman and Swerts
  • June 2004: Teaching Computers to Teach Like Humans, Pitt Chronicle

    Teaching

  • Spring 2009: Speech and Natural Language Processing for Educational Applications (CS 3710/ISSP 3565: Advanced Topics in Artificial Intelligence)

  • CS 0441: Discrete Structures for Computer Science (Spring 2005, Fall 2006, Spring 2007)
  • CS 1571: Introduction to Artificial Intelligence (Fall 2008)
  • CS 1573: Artificial Intelligence Application Development (Spring 2003, 2004)
  • CS 2710 / ISSP 2160: Foundations of Artificial Intelligence (Fall 2006)
  • CS 2731 / ISSP 2230: Introduction to Natural Language Processing (Fall 2001, 2002, 2003, 2004)
  • CS 3710 / ISSP 3565: Advanced Topics in Artificial Intelligence (Dialog Systems) (Spring 2002)
  • CS 3710 / ISSP 3565: Advanced Topics in Artificial Intelligence (Affective Spoken Dialogue Systems) (Spring 2006)
  • CS 3710 / ISSP 3565: Advanced Topics in Artificial Intelligence (Speech and Natural Language Processing for Educational Applications) (Spring 2009)

    2009 Activities

  • Senior Programme Committee, 14th International Conference of Artificial Intelligence in Education (AIED)
  • Program Committee, International Conference on Affective Computing and Intelligent Interaction (ACII)
  • Program Committee, The 13th Workshop on the Semantics and Pragmatics of Dialogue (DIAHOLMIA)
  • Program Committee, The 4th Workshop on Innovative Use of NLP for Building Educational Applications (NAACL-HLT 2009 Workshop)
  • Program Committee, 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)
  • Reviewer, Annual Meeting of the Association for Computational Linguistics (ACL)

    • Brief Biography

    I am Professor of Computer Science, and a Research Scientist with the Learning Research and Development Center (LRDC), at the University of Pittsburgh. I moved here from the Garden State (aka New Jersey), where from 1985-2001 I was a member of the Artificial Intelligence Principles Research Department, AT&T Labs - Research (formerly Bell Laboratories); From 1990-1992, I was also an Assistant Professor of Computer Science at Columbia University. I received my Ph.D. and M.S. in Computer Science from the University of Rochester, and my A.B. in Mathematics and Computer Science from the College of William and Mary in Virginia. Here is my academic geneology.

    Some Personal Stuff , My Car

    Selected Publications (by topic)

  • Spoken Dialogue and Affect for Intelligent Tutoring Systems: Spoken dialogue is a natural and highly desirable form of student-computer interaction, which provides both opportunities and challenges to both dialogue-based tutoring systems, and to spoken language systems. The goal of my research is to wed spoken language technology with instructional technology, in order to promote learning gains by enhancing communication richness. For further details, see the ITSPOKE webpage, which contains information on the ITSPOKE system and corpora, as well as information on the back-end (Why2, a text-based tutoring system in the domain of qualitative physics).

  • Exceptionality and Natural Language Learning: Previous work has shown that when machine learning is applied to many natural language processing tasks, exceptional training examples play an important role in improving generalization accuracy. We are exploring whether such results generalize to spoken dialogue, and how different formalizations of "exceptionality" impact the performance of memory-based and rule-based learning algorithms. Click for further details and online publications.

  • Question Answering: The development of resources for extending the current automatic question-answering paradigm to hande opinion-oriented, rather than fact-oriented, questions. Also, the use of ensemble methods to combine the output of multiple QA systems to improve performance, in the reading comprehension domain. Click for further details and online publications.

  • Spoken Dialogue for CHAT: The design, implementation, and empirical experiences of CobotDS, a spoken dialogue system for accessing the LambdaMoo text-based chat environment. CobotDS allows phone users to talk with LambdaMoo users via Cobot, a software agent residing in LambdaMOO. Click for further details and online publications.

  • Reinforcement Learning for Optimizing Spoken Dialogue Agents: The use of reinforcement learning to analyze and optimize dialogue strategy design in spoken dialogue systems. An empirical evaluation of an automatically optimized dialogue manager. Click for further details and online publications.

  • Prosodic Analysis of Misrecognitions and Corrections in Spoken Dialogue: Analytic and machine learning results indicating how prosodic differences can predict misrecognized vs. correctly recognized turns, and correction vs. other types of utterances. Click for further details and online publications.

  • Learning how to Predict Problematic Dialogue Situations, and an Application to Adaptive Spoken Dialogue: The use of rule induction to predict problematic dialogue situations (e.g. poor speech recognition, "bail out" situations where a caller should be transferred to a human operator). The design and evaluation of a spoken dialogue system that adapts its behavior when problematic situations are detected. Click for further details and online publications.

  • Evaluating Spoken Dialogue Agents: The PARADISE (PARAdigm for DIalogue System Evaluation) framework for empirically deriving an objective performance function, and PARADISE evaluations of cooperative responses, the use of tutorial dialogues, and adaptable dialogue behavior. Click for further details and online publications.

  • Device Representation and Reasoning with Affective Relations, and an Implementation in R++: An approach to monitoring and diagnosis of complex systems that integrates classical model-based diagnosis and heuristic expert systems. Implemented using R++, an extension of C++ that incorporates rules into the object-oriented paradigm. Click for further details, and online publications.

  • Terminological Reasoning with Plans and an Application to Plan Recognition: A plan-based knowledge representation system that integrates description logics with metric and qualitative temporal constraint languages, and a companion plan recognition system (T-REX) that uses the plan-based description logic. CLASP is an alternative plan-based knowledge representation system that extends description logics to handle temporal information by representing plans as regular expressions. Click for further details and online publications.

  • A Corpus-Based Approach to Classifying Discourse Segment Boundaries and Cue Phrases in Text and Speech: Empirical discourse analysis, with an emphasis on coding of data, the use of machine learning for hypothesis formation, and quantitative evaluation of results. Click for further details and online publications.

  • Plan Recognition: The use of plan recognition in both natural language dialogue systems and in intelligent graphical interfaces. Click for further details and online publications.

    • Offices

  • Past Chair, North American Chapter of the Association for Computational Linguistics (NAACL) , 2004-2005
  • International Advisory Committee for the ACL's Special Interest Group on Natural Language Learning (SIGNLL), 2003-present
  • Advisory Board, UM Inc., 2002-present

    Memberships

  • American Association for Artificial Intelligence (AAAI)
  • Association for Computational Linguistics (ACL) (also SIGDIAL, SIGGEN, SIGNLL)
  • International Artificial Intelligence in Education Society (AIED)
  • International Speech Communication Association (ISCA)
  • Provost's Advisory Committee on Women's Concerns at the University of Pittsburgh (2002-2005)
  • American Daffodil Society
    December 2006
    litman at cs dot pitt dot edu