Department of Computer Science

Machine Learning Research Group

University of Texas at Austin Artificial Intelligence Lab

Publications: 2020

  1. Systematic Generalization on gSCAN with Language Conditioned Embedding
    [Details] [PDF] [Video]
    Tong Gao, Qi Huang and Raymond J. Mooney
    In The 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing , December 2020.
    Systematic Generalization refers to a learning algorithm’s ability to extrapolate learned behavior to unseen situations that are distinct but semantically similar to its training data. As shown in recent work, state-of-the-art deep learning models fail dramatically even on tasks for which they are designed when the test set is systematically different from the training data. We hypothesize that explicitly modeling the relations between objects in their contexts while learning their representations will help achieve systematic generalization. There- fore, we propose a novel method that learns objects’ contextualized embedding with dynamic message-passing conditioned on the input natural language and is end-to-end trainable with other downstream deep learning modules. To our knowledge, this model is the first one that significantly outperforms the provided baseline and reaches state-of-the-art performance on grounded SCAN (gSCAN), a grounded natural language navigation dataset designed to require systematic generalization in its test splits.
    ML ID: 390
  2. Dialog as a Vehicle for Lifelong Learning of Grounded Language Understanding Systems
    [Details] [PDF] [Slides (PDF)]
    Aishwarya Padmakumar
    PhD Thesis, Department of Computer Science, The University of Texas at Austin, August 2020.
    Natural language interfaces have the potential to make various forms of technology, including mobile phones and computers as well as robots or other machines such as ATMs and self-checkout counters, more accessible and less intimidating to users who are unfamiliar or uncomfortable with other types of interfaces. In particular, natural language understanding systems on physical robots face a number of challenges, including the need to ground language in perception, the ability to adapt to changes in the environment and novel uses of language, and to deal with uncertainty in understanding. To effectively handle these challenges, such systems need to perform lifelong learning - continually updating the scope and predictions of the model with user interactions. In this thesis, we discuss ways in which dialog interaction with users can be used to improve grounded natural language understanding systems, motivated by service robot applications. We focus on two types of queries that can be used in such dialog systems – active learning queries to elicit knowledge about the environment that can be used to improve perceptual models, and clarification questions that confirm the system’s hypotheses, or elicit specific information required to complete a task. Our goal is to build a system that can learn how to interact with users balancing a quick completion of tasks desired by the user with asking additional active learning questions to improve the underlying grounded language understanding components. We present work on jointly improving semantic parsers from and learning a dialog policy for clarification dialogs, that improve a robot’s ability to understand natural language commands. We introduce the framework of opportunistic active learning, where a robot introduces opportunistic queries, that may not be immediately relevant, into an interaction in the hope of improving performance in future interactions. We demonstrate the usefulness of this framework in learning to ground natural language descriptions of objects, and learn a dialog policy for such interactions. We also learn dialog policies that balance task completion, opportunistic active learning, and attribute-based clarification questions. Finally, we attempt to expand this framework to different types of underlying models of grounded language understanding.
    ML ID: 389
  3. PixL2R: Guiding Reinforcement Learning using Natural Language by Mapping Pixels to Rewards
    [Details] [PDF]
    Prasoon Goyal, Scott Niekum, Raymond J. Mooney
    In 4th Conference on Robot Learning (CoRL), November 2020. Also presented on the 1st Language in Reinforcement Learning (LaReL) Workshop at ICML, July 2020 (Best Paper Award), the 6th Deep Reinforcement Learning Workshop at Neural Information Processing Systems (NeurIPS), Dec 2020.
    Reinforcement learning (RL), particularly in sparse reward settings, often requires prohibitively large numbers of interactions with the environment, thereby limiting its applicability to complex problems. To address this, several prior approaches have used natural language to guide the agent's exploration. However, these approaches typically operate on structured representations of the environment, and/or assume some structure in the natural language commands. In this work, we propose a model that directly maps pixels to rewards, given a free-form natural language description of the task, which can then be used for policy training. Our experiments on the Meta-World robot manipulation domain show that language-based rewards significantly improve learning. Further, we analyze the resulting framework using multiple ablation experiments to better understand the nature of these improvements.
    ML ID: 388
  4. Dialog as a Vehicle for Lifelong Learning
    [Details] [PDF] [Slides (PDF)] [Video]
    Aishwarya Padmakumar, Raymond J. Mooney
    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
  5. 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
  6. Learning to Update Natural Language Comments Based on Code Changes
    [Details] [PDF] [Video]
    Sheena Panthaplackel, Pengyu Nie, Milos Gligoric, Junyi Jessy Li, and Raymond J. Mooney
    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
  7. 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
  8. 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