Department of Computer Science

Machine Learning Research Group

University of Texas at Austin Artificial Intelligence Lab

Publications: 2018

  1. Generating Animated Videos of Human Activities from Natural Language Descriptions
    [Details] [PDF] [Poster]
    Angela S. Lin, Lemeng Wu, Rodolfo Corona , Kevin Tai , Qixing Huang , Raymond J. Mooney
    In Proceedings of the Visually Grounded Interaction and Language Workshop at NeurIPS 2018, December 2018.
    Generating realistic character animations is of great importance in computer graphics and related domains. Existing approaches for this application involve a significant amount of human interaction. In this paper, we introduce a system that maps a natural language description to an animation of a humanoid skeleton. Our system is a sequence-to-sequence model that is pretrained with an autoencoder objective and then trained end-to-end.
    ML ID: 369
  2. Learning a Policy for Opportunistic Active Learning
    [Details] [PDF]
    Aishwarya Padmakumar, Peter Stone, Raymond J. Mooney
    In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP-18), Brussels, Belgium, November 2018.
    Active learning identifies data points to label that are expected to be the most useful in improving a supervised model. Opportunistic active learning incorporates active learning into interactive tasks that constrain possible queries during interactions. Prior work has shown that opportunistic active learning can be used to improve grounding of natural language descriptions in an interactive object retrieval task. In this work, we use reinforcement learning for such an object retrieval task, to learn a policy that effectively trades off task completion with model improvement that would benefit future tasks.
    ML ID: 368
  3. Improved Models and Queries for Grounded Human-Robot Dialog
    [Details] [PDF]
    Aishwarya Padmakumar
    October 2018. PhD Proposal, Department of Computer Science, The University of Texas At Austin.
    The ability to understand and communicate in natural language can make robots much more accessible for naive users. Environments such as homes and offices contain many objects that humans describe in diverse language referencing perceptual properties. Robots operating in such environments need to be able to understand such descriptions. Different types of dialog interactions with humans can help robots clarify their understanding to reduce mistakes, and also improve their language understanding models, or adapt them to the specific domain of operation. We present completed work on jointly learning a dialog policy that enables a robot to clarify partially understood natural language commands, while simultaneously using the dialogs to improve the underlying semantic parser for future commands. We introduce the setting of opportunistic active learning - a framework for interactive tasks that use supervised models. This framework allows a robot to ask diverse, potentially off-topic queries across interactions, requiring the robot to trade-off between task completion and knowledge acquisition for future tasks. We also attempt to learn a dialog policy in this framework using reinforcement learning. We propose a novel distributional model for perceptual grounding, based on learning a joint space for vector representations from multiple modalities. We also propose a method for identifying more informative clarification questions that can scale well to a larger space of objects, and wish to learn a dialog policy that would make use of such clarifications.
    ML ID: 367
  4. Interaction and Autonomy in RoboCup@Home and Building-Wide Intelligence
    [Details] [PDF]
    Justin Hart, Harel Yedidsion, Yuqian Jiang, Nick Walker, Rishi Shah, Jesse Thomason, Aishwarya Padmakumar, Rolando Fernandez, Jivko Sinapov, Raymond Mooney, Peter Stone
    In Artificial Intelligence (AI) for Human-Robot Interaction (HRI) symposium, AAAI Fall Symposium Series, Arlington, Virginia, October 2018.
    Efforts are underway at UT Austin to build autonomous robot systems that address the challenges of long-term deployments in office environments and of the more prescribed domestic service tasks of the RoboCup@Home competition. We discuss the contrasts and synergies of these efforts, highlighting how our work to build a RoboCup@Home Domestic Standard Platform League entry led us to identify an integrated software architecture that could support both projects. Further, naturalistic deployments of our office robot platform as part of the Building-Wide Intelligence project have led us to identify and research new problems in a traditional laboratory setting.
    ML ID: 366
  5. 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, and Raymond J. Mooney
    In Late-breaking Track at the SIGDIAL Special Session on Physically Situated Dialogue (RoboDIAL-18), Melbourne, Australia, July 2018.
    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. We combine these steps towards language understanding into a holistic agent for jointly improving parsing and perception on a robotic platform through human-robot dialog. We train and evaluate this agent on Amazon Mechanical Turk, then demonstrate it on a robotic platform initialized from that conversational data. Our experiments show that improving both parsing and perception components from conversations improves communication quality and human ratings of the agent.
    ML ID: 365
  6. Explainable Improved Ensembling for Natural Language and Vision
    [Details] [PDF] [Slides (PPT)] [Slides (PDF)]
    Nazneen Rajani
    PhD Thesis, Department of Computer Science, The University of Texas at Austin, July 2018.
    Ensemble methods are well-known in machine learning for improving prediction accuracy. However, they do not adequately discriminate among underlying component models. The measure of how good a model is can sometimes be estimated from “why” it made a specific prediction. We propose a novel approach called Stacking With Auxiliary Features (SWAF) that effectively leverages component models by integrating such relevant information from context to improve ensembling. Using auxiliary features, our algorithm learns to rely on systems that not just agree on an output prediction but also the source or origin of that output. We demonstrate our approach to challenging structured prediction problems in Natural Language Processing and Vision including Information Extraction, Object Detection, and Visual Question Answering. We also present a variant of SWAF for combining systems that do not have training data in an unsupervised ensemble with systems that do have training data. Our combined approach obtains a new state-of-the-art, beating our prior performance on Information Extraction. The state-of-the-art systems on many AI applications are ensembles of deep-learning models. These models are hard to interpret and can sometimes make odd mistakes. Explanations make AI systems more transparent and also justify their predictions. We propose a scalable approach to generate visual explanations for ensemble methods using the localization maps of the component systems. Crowdsourced human evaluation on two new metrics indicates that our ensemble’s explanation significantly qualitatively outperforms individual systems’ explanations.
    ML ID: 364
  7. 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, and Raymond J. Mooney
    In RSS Workshop on Models and Representations for Natural Human-Robot Communication (MRHRC-18). Robotics: Science and Systems (RSS), June 2018.
    Natural language understanding in robots needs to be robust to a wide-range of both human speakers and human environments. Rather than force humans to use language that robots can understand, robots in human environments should dynamically adapt—continuously learning new language constructions and perceptual concepts as they are used in context. 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. We combine these steps towards language understanding into a holistic agent for jointly improving parsing and perception on a robotic platform through human-robot dialog. We train and evaluate this agent on Amazon Mechanical Turk, then demonstrate it on a robotic platform initialized from conversational data gathered from Mechanical Turk. Our experiments show that improving both parsing and perception components from conversations improves communication quality and human ratings of the agent.
    ML ID: 363
  8. Joint Image Captioning and Question Answering
    [Details] [PDF] [Poster]
    Jialin Wu, Zeyuan Hu and Raymond J. Mooney
    In VQA Challenge and Visual Dialog Workshop at the 31st IEEE Conference on Computer Vision and Pattern Recognition (CVPR-18) , June 2018.
    Answering visual questions need acquire daily common knowledge and model the semantic connection among different parts in images, which is too difficult for VQA systems to learn from images with the only supervision from answers. Meanwhile, image captioning systems with beam search strategy tend to generate similar captions and fail to diversely describe images. To address the aforementioned issues, we present a system to have these two tasks compensate with each other, which is capable of jointly producing image captions and answering visual questions. In particular, we utilize question and image features to generate question-related captions and use the generated captions as additional features to provide new knowledge to the VQA system. For image captioning, our system attains more informative results in term of the relative improvements on VQA tasks as well as competitive results using automated metrics. Applying our system to the VQA tasks, our results on VQA v2 dataset achieve 65.8% using generated captions and 69.1% using annotated captions in validation set and 68.4% in the test-standard set. Further, an ensemble of 10 models results in 69.7% in the test-standard split.
    ML ID: 362
  9. Continually Improving Grounded Natural Language Understanding through Human-Robot Dialog
    [Details] [PDF]
    Jesse Thomason
    PhD Thesis, Department of Computer Science, The University of Texas at Austin, April 2018.
    As robots become ubiquitous in homes and workplaces such as hospitals and factories, they must be able to communicate with humans. Several kinds of knowledge are required to understand and respond to a human's natural language commands and questions. If a person requests an assistant robot to "take me to Alice's office", the robot must know that Alice is a person who owns some unique office, and that "take me" means it should navigate there. Similarly, if a person requests "bring me the heavy, green mug", the robot must have accurate mental models of the physical concepts "heavy", "green", and "mug". To avoid forcing humans to use key phrases or words robots already know, this thesis focuses on helping robots understanding new language constructs through interactions with humans and with the world around them. To understand a command in natural language, a robot must first convert that command to an internal representation that it can reason with. Semantic parsing is a method for performing this conversion, and the target representation is often semantic forms represented as predicate logic with lambda calculus. Traditional semantic parsing relies on hand-crafted resources from a human expert: an ontology of concepts, a lexicon connecting language to those concepts, and training examples of language with abstract meanings. One thrust of this thesis is to perform semantic parsing with sparse initial data. We use the conversations between a robot and human users to induce pairs of natural language utterances with the target semantic forms a robot discovers through its questions, reducing the annotation effort of creating training examples for parsing. We use this data to build more dialog-capable robots in new domains with much less expert human effort. Meanings of many language concepts are bound to the physical world. Understanding object properties and categories, such as "heavy", "green", and "mug" requires interacting with and perceiving the physical world. Embodied robots can use manipulation capabilities, such as pushing, picking up, and dropping objects to gather sensory data about them. This data can be used to understand non-visual concepts like "heavy" and "empty" (e.g. "get the empty carton of milk from the fridge"), and assist with concepts that have both visual and non-visual expression (e.g. "tall" things look big and also exert force sooner than "short" things when pressed down on). A second thrust of this thesis focuses on strategies for learning these concepts using multi-modal sensory information. We use human-in-the-loop learning to get labels between concept words and actual objects in the environment. We also explore ways to tease out polysemy and synonymy in concept words such as "light", which can refer to a weight or a color, the latter sense being synonymous with "pale". Additionally, pushing, picking up, and dropping objects to gather sensory information is prohibitively time-consuming, so we investigate strategies for using linguistic information and human input to expedite exploration when learning a new concept. Finally, we build an integrated agent with both parsing and perception capabilities that learns from conversations with users to improve both components over time. We demonstrate that parser learning from conversations can be combined with multi-modal perception using predicate-object labels gathered through opportunistic active learning during those conversations to improve performance for understanding natural language commands from humans. Human users also qualitatively rate this integrated learning agent as more usable after it has improved from conversation-based learning.
    ML ID: 361
  10. Stacking With Auxiliary Features for Visual Question Answering
    [Details] [PDF] [Poster]
    Nazneen Fatema Rajani, Raymond J. Mooney
    In Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2217-2226, 2018.
    Visual Question Answering (VQA) is a well-known and challenging task that requires systems to jointly reason about natural language and vision. Deep learning models in various forms have been the standard for solving VQA. However, some of these VQA models are better at certain types of image-question pairs than other models. Ensembling VQA models intelligently to leverage their diverse expertise is, therefore, advantageous. Stacking With Auxiliary Features (SWAF) is an intelligent ensembling technique which learns to combine the results of multiple models using features of the current problem as context. We propose four categories of auxiliary features for ensembling for VQA. Three out of the four categories of features can be inferred from an image-question pair and do not require querying the component models. The fourth category of auxiliary features uses model-specific explanations. In this paper, we describe how we use these various categories of auxiliary features to improve performance for VQA. Using SWAF to effectively ensemble three recent systems, we obtain a new state-of-the-art. Our work also highlights the advantages of explainable AI models.
    ML ID: 360
  11. 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
  12. Guiding Exploratory Behaviors for Multi-Modal Grounding of Linguistic Descriptions
    [Details] [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
  13. Multi-modal Predicate Identification using Dynamically Learned Robot Controllers
    [Details] [PDF]
    Saeid Amiri and Suhua Wei and Shiqi Zhang and Jivko Sinapov and Jesse Thomason and Peter Stone
    In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI-18), Stockholm, Sweden, July 2018.
    Intelligent robots frequently need to explore the objects in their working environments. Modern sensors have enabled robots to learn object properties via perception of multiple modalities. However, object exploration in the real world poses a challenging trade-off between information gains and exploration action costs. Mixed observability Markov decision process (MOMDP) is a framework for planning under uncertainty, while accounting for both fully and partially observable components of the state. Robot perception frequently has to face such mixed observability. This work enables a robot equipped with an arm to dynamically construct query-oriented MOMDPs for multi-modal predicate identification (MPI) of objects. The robot's behavioral policy is learned from two datasets collected using real robots. Our approach enables a robot to explore object properties in a way that is significantly faster while improving accuracies in comparison to existing methods that rely on hand-coded exploration strategies.