Department of Computer Science

Machine Learning Research Group

University of Texas at Austin Artificial Intelligence Lab

Publications: 2020

  1. Dialog as a Vehicle for Lifelong Learning
    [Details] [PDF] [Slides (PDF)] [Video]
    Aishwarya Padmakumar, Raymond J. Mooney
    To Appear In Position Paper Track at the SIGDIAL Special Session on Physically Situated Dialogue (RoboDial 2.0), July 2020.
    Dialog systems research has primarily been focused around two main types of applications – task-oriented dialog systems that learn to use clarification to aid in understanding a goal, and open-ended dialog systems that are expected to carry out unconstrained “chit chat” conversations. However, dialog interactions can also be used to obtain various types of knowledge that can be used to improve an underlying language understanding system, or other machine learning systems that the dialog acts over. In this position paper, we present the problem of designing dialog systems that enable lifelong learning as an important challenge problem, in particular for applications involving physically situated robots. We include examples of prior work in this direction, and discuss challenges that remain to be addressed.
    ML ID: 386
  2. Dialog Policy Learning for Joint Clarification and Active Learning Queries
    [Details] [PDF]
    Aishwarya Padmakumar, Raymond J. Mooney
    Computing Research Repository, arXiv:2006.05456, June 2020.
    Intelligent systems need to be able to recover from mistakes, resolve uncertainty, and adapt to novel concepts not seen during training. Dialog interaction can enable this by the use of clarifications for correction and resolving uncertainty, and active learning queries to learn new concepts encountered during operation. Prior work on dialog systems has either focused on exclusively learning how to perform clarification/ information seeking, or to perform active learning. In this work, we train a hierarchical dialog policy to jointly perform both clarification and active learning in the context of an interactive language-based image retrieval task motivated by an on-line shopping application, and demonstrate that jointly learning dialog policies for clarification and active learning is more effective than the use of static dialog policies for one or both of these functions.
    ML ID: 385
  3. Evaluating the Robustness of Natural Language Reward Shaping Models to Spatial Relations
    [Details] [PDF] [Slides (PPT)] [Slides (PDF)]
    Antony Yun
    May 2020. Undergraduate Honors Thesis, Computer Science Department, University of Texas at Austin.
    As part of an effort to bridge the gap between using reinforcement learning in simulation and in the real world, we probe whether current reward shaping models are able to encode relational data between objects in the environment. We construct an augmented dataset for controlling a robotic arm in the Meta-World platform to test whether current models are able to discriminate between target objects based on their relations. We found that state of the art models are indeed expressive enough to achieve performance comparable to the gold standard, so this specific experiment did not uncover any obvious shortcomings.
    ML ID: 384
  4. Learning to Update Natural Language Comments Based on Code Changes
    [Details] [PDF]
    Sheena Panthaplackel, Pengyu Nie, Milos Gligoric, Junyi Jessy Li, and Raymond J. Mooney
    To Appear In Proceedings of the 58th Annual Conference of the Association for Computational Linguistics (ACL), July 2020.
    We formulate the novel task of automatically updating an existing natural language comment based on changes in the body of code it accompanies. We propose an approach that learns to correlate changes across two distinct language representations, to generate a sequence of edits that are applied to the existing comment to reflect the source code modifications. We train and evaluate our model using a dataset that we collected from commit histories of open-source software projects, with each example consisting of a concurrent update to a method and its corresponding comment. We compare our approach against multiple baselines using both automatic metrics and human evaluation. Results reflect the challenge of this task and that our model outperforms baselines with respect to making edits.
    ML ID: 383
  5. Associating Natural Language Comment and Source Code Entities
    [Details] [PDF] [Slides (PDF)] [Poster]
    Sheena Panthaplackel, Milos Gligoric, Raymond J. Mooney and Junyi Jessy Li
    In The AAAI Conference on Artificial Intelligence (AAAI), February 2020.
    Comments are an integral part of software development; they are natural language descriptions associated with source code elements. Understanding explicit associations can be useful in improving code comprehensibility and maintaining the consistency between code and comments. As an initial step towards this larger goal, we address the task of associating entities in Javadoc comments with elements in Java source code. We propose an approach for automatically extracting supervised data using revision histories of open source projects and present a manually annotated evaluation dataset for this task. We develop a binary classifier and a sequence labeling model by crafting a rich feature set which encompasses various aspects of code, comments, and the relationships between them. Experiments show that our systems outperform several baselines learning from the proposed supervision.
    ML ID: 382
  6. Jointly Improving Parsing and Perception for Natural Language Commands through Human-Robot Dialog
    [Details] [PDF]
    Jesse Thomason, Aishwarya Padmakumar, Jivko Sinapov, Nick Walker, Yuqian Jiang, Harel Yedidsion, Justin Hart, Peter Stone, Raymond J. Mooney
    The Journal of Artificial Intelligence Research (JAIR), 67:327-374, February 2020.
    Humans use natural language to articulate their thoughts and intentions to other people, making it a natural channel for human-robot communication. Natural language understanding in robots needs to be robust to a wide-range of both human speakers and environments. In this work, we present methods for parsing natural language to underlying meanings and using robotic sensors to create multi-modal models of perceptual concepts. Through dialog, robots should learn new language constructions and perceptual concepts as they are used in context. We develop an agent for jointly improving parsing and perception in simulation through human-robot dialog, and demonstrate this agent on a robotic platform. Dialog clarification questions are used both to understand commands and to generate additional parsing training data. The agent improves its perceptual concept models through questions about how words relate to objects. We evaluate this agent on Amazon Mechanical Turk. After training on induced data from conversations, the agent can reduce the number of clarification questions asked while receiving higher usability ratings. Additionally, we demonstrate the agent on a robotic platform, where it learns new concepts on the fly while completing a real-world task.
    ML ID: 381