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Dr. Sarah Chen
Stanford · Interpretability · NLP
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Nova · Professor Researcher · re-ranking top 20…
Stefanie Tellex

Stefanie Tellex

Verified

Brown University · Linguistics

Active 2002–2024

h-index45
Citations6.0k
Papers20185 last 5y
Funding$1.4M
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Research topics

  • Computer Science
  • Artificial Intelligence
  • Natural Language Processing
  • Human–computer interaction
  • Geography

Selected publications

  • Spoken language interaction with robots: Recommendations for future research

    Computer Speech & Language · 2021 · 105 citations

    • Computer Science
    • Computer Science
    • Natural Language Processing

    With robotics rapidly advancing, more effective human–robot interaction is increasingly needed to realize the full potential of robots for society. While spoken language must be part of the solution, our ability to provide spoken language interaction capabilities is still very limited. In this article, based on the report of an interdisciplinary workshop convened by the National Science Foundation, we identify key scientific and engineering advances needed to enable effective spoken language interaction with robotics. We make 25 recommendations, involving eight general themes: putting human needs first, better modeling the social and interactive aspects of language, improving robustness, creating new methods for rapid adaptation, better integrating speech and language with other communication modalities, giving speech and language components access to rich representations of the robot’s current knowledge and state, making all components operate in real time, and improving research infrastructure and resources. Research and development that prioritizes these topics will, we believe, provide a solid foundation for the creation of speech-capable robots that are easy and effective for humans to work with.

  • Robots That Use Language

    Annual Review of Control Robotics and Autonomous Systems · 2020 · 202 citations

    1st authorCorresponding
    • Artificial Intelligence
    • Computer Science
    • Artificial Intelligence

    This article surveys the use of natural language in robotics from a robotics point of view. To use human language, robots must map words to aspects of the physical world, mediated by the robot's sensors and actuators. This problem differs from other natural language processing domains due to the need to ground the language to noisy percepts and physical actions. Here, we describe central aspects of language use by robots, including understanding natural language requests, using language to drive learning about the physical world, and engaging in collaborative dialogue with a human partner. We describe common approaches, roughly divided into learning methods, logic-based methods, and methods that focus on questions of human–robot interaction. Finally, we describe several application domains for language-using robots.

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