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Computer Science

D-Index
65
Citations
13645
World Ranking
2496
National Ranking
1246

Overview

Diane J. Litman is affiliated with the University of Pittsburgh in the United States. Their research spans multiple fields, including computer science and social sciences, with a focus on artificial intelligence, education, developmental and educational psychology, information systems, and political science and international relations.

Their main research topics cover:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Speech and dialogue systems
  • Innovative Teaching and Learning Methods
  • Software Engineering Research
  • Writing and Handwriting Education
  • Online Learning and Analytics

Frequent publication venues include:

  • arXiv (Cornell University)
  • Assessing Writing
  • Proceedings
  • Technology Mind and Behavior
  • Computers and Education Open

Recent papers authored or co-authored by Diane J. Litman include:

  • "eRevis(ing): Students' revision of text evidence use in an automated writing evaluation system" (2020, Assessing Writing)
  • "Building a validity argument for an automated writing evaluation system (eRevise) as a formative assessment" (2022, Computers and Education Open)
  • "Toward more effective and equitable learning: Identifying barriers and solutions for the future of online education." (2022, Technology Mind and Behavior)
  • "ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining" (2022, arXiv [Cornell University])
  • "ArgRewrite V.2: an annotated argumentative revisions corpus" (2022, Language Resources and Evaluation)

Diane J. Litman's frequent co-authors include:

  • Lindsay Clare Matsumura
  • Richard Correnti
  • Elaine Wang
  • Adriana Kovashka
  • Timothy J. Nokes-Malach

In addition to articles, Diane J. Litman has contributed to book publications such as Contributions to Research on Automated Writing Scoring and Feedback Systems published by RAND Corporation eBooks in 2022.

Best Publications

  • PARADISE: A Framework for Evaluating Spoken Dialogue Agents

    Marilyn A. Walker;Diane J. Litman;Candace A. Kamm;Alicia Abella

  • A plan recognition model for subdialogues in conversations

    Diane J. Litman;James F. Allen

  • Empirical studies on the disambiguation of cue phrases

    Julia Hirschberg;Diane Litman

  • Optimizing dialogue management with reinforcement learning: experiments with the NJFun system

    Satinder Singh;Diane Litman;Michael Kearns;Marilyn Walker

  • Towards developing general models of usability with PARADISE

    Marilyn Walker;Candace Kamm;Diane Litman

  • ITSPOKE: an intelligent tutoring spoken dialogue system

    Diane J. Litman;Scott Silliman

  • Discourse segmentation by human and automated means

    Rebecca J. Passonneau;Diane J. Litman

  • Reinforcement Learning for Spoken Dialogue Systems

    Satinder P. Singh;Michael J. Kearns;Diane J. Litman;Marilyn A. Walker

  • Predicting Student Emotions in Computer-Human Tutoring Dialogues

    Diane J. Litman;Kate Forbes-Riley

  • Designing and Evaluating an Adaptive Spoken Dialogue System

    Diane J. Litman;Shimei Pan

  • Evaluating spoken dialogue agents with PARADISE: Two case studies

    Marilyn A. Walker;Diane J. Litman;Candace A. Kamm;Alicia Abella

  • INTENTION-BASED SEGMENTATION: HUMAN RELIABILITY AND CORRELATION WITH LINGUISTIC CUES

    Rebecca J. Passonneau;Diane J. Litman

  • Method and system for predicting understanding errors in automated dialog systems

    Allen Louis Gorin;Irene Langkilde Geary;Marilyn Ann Walker;Jeremy H. Wright

  • Empirically evaluating the application of reinforcement learning to the induction of effective and adaptive pedagogical strategies

    Min Chi;Kurt Vanlehn;Diane Litman;Pamela Jordan

  • Recognizing student emotions and attitudes on the basis of utterances in spoken tutoring dialogues with both human and computer tutors

    Diane J. Litman;Katherine Forbes-Riley

  • Plan recognition and discourse analysis: an integrated approach for understanding dialogues

    Diane Judith Litman

  • Benefits and challenges of real-time uncertainty detection and adaptation in a spoken dialogue computer tutor

    Kate Forbes-Riley;Diane Litman

  • Combining Multiple Knowledge Sources for Discourse Segmentation

    Diane J. Litman;Rebecca J. Passonneau

  • NOW LET'S TALK ABOUT NOW; IDENTIFYING CUE PHRASES INTONATIONALLY

    Julia Hirschberg;Diane Litman

  • Learning to predict problematic situations in a spoken dialogue system: experiments with how may I help you?

    Marilyn Walker;Irene Langkilde;Jerry Wright;Allen Gorin

  • Spoken Versus Typed Human and Computer Dialogue Tutoring

    Diane J. Litman;Carolyn P. Rosé;Kate Forbes-Riley;Kurt Vanlehn

Frequent Co-Authors

Marilyn A. Walker
Marilyn A. Walker University of California, Santa Cruz
Julia Hirschberg
Julia Hirschberg Columbia University
Kurt VanLehn
Kurt VanLehn Arizona State University
Marc Swerts
Marc Swerts Tilburg University
Rebecca J. Passonneau
Rebecca J. Passonneau Pennsylvania State University
Joel Tetreault
Joel Tetreault Yahoo (United Kingdom)
Claire Cardie
Claire Cardie Cornell University
Satinder Singh
Satinder Singh DeepMind (United Kingdom)
Janyce Wiebe
Janyce Wiebe University of Pittsburgh
Michael Kearns
Michael Kearns University of Pennsylvania

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