Department of Computer Science

Machine Learning Research Group

University of Texas at Austin Artificial Intelligence Lab

Publications: 2018

  1. Natural Language Processing and Program Analysis for Supporting Todo Comments as Software Evolves
    [Details] [PDF]
    Pengyu Nie, Junyi Jessy Li, Sarfraz Khurshid, Raymond Mooney, Milos Gligoric
    In In Proceedings of the AAAI Workshop on NLP for Software Engineering, February 2018.
    Natural language elements (e.g., API comments, todo comments) form a substantial part of software repositories. While developers routinely use many natural language elements (e.g., todo comments) for communication, the semantic content of these elements is often neglected by software engineering techniques and tools. Additionally, as software evolves and development teams re-organize, these natural language elements are frequently forgotten, or just become outdated, imprecise and irrelevant. We envision several techniques, which combine natural language processing and program analysis, to help developers maintain their todo comments. Specifically, we propose techniques to synthesize code from comments, make comments executable, answer questions in comments, improve comment quality, and detect dangling comments.
    ML ID: 358
  2. Guiding Exploratory Behaviors for Multi-Modal Grounding of Linguistic Descriptions
    [Details] [PDF] [Slides (PDF)]
    Jesse Thomason, Jivko Sinapov, Raymond Mooney, Peter Stone
    In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) , February 2018.
    A major goal of grounded language learning research is to enable robots to connect language predicates to a robot’s physical interactive perception of the world. Coupling object exploratory behaviors such as grasping, lifting, and looking with multiple sensory modalities (e.g., audio, haptics, and vision) enables a robot to ground non-visual words like “heavy” as well as visual words like “red”. A major limitation of existing approaches to multi-modal language grounding is that a robot has to exhaustively explore training objects with a variety of actions when learning a new such language predicate. This paper proposes a method for guiding a robot’s behavioral exploration policy when learning a novel predicate based on known grounded predicates and the novel predicate’s linguistic relationship to them. We demonstrate our approach on two datasets in which a robot explored large sets of objects and was tasked with learning to recognize whether novel words applied to those objects.
    ML ID: 357