On sample efficiency and systematic generalization of grounded language understanding with deep learning

By using the methodology of deep learning that advocates relying more on data and flexible neural models rather than on the expert's knowledge of the domain, the research community has recently achieved remarkable progress in natural language understanding and generation. Nevertheless, it remains unclear whether simply scaling up existing deep learning methods will be sufficient to achieve the goal of using natural language for human-computer interaction. We focus on two related aspects in which current methods appear to require major improvements. The first such aspect is the data inefficiency of deep learning systems: they are known to require extreme amounts of data to perform well. The second aspect is their limited ability to generalize systematically, namely to understand language in situations when the data distribution changes yet the principles of syntax and semantics remain the same.   In this thesis, we present four case studies in which we seek to provide more clarity regarding the aforementioned data efficiency and systematic generalization aspects of deep learning approaches to language understanding, as well as to facilitate further work on these topics.  In order to separate the problem of representing open-ended real-world knowledge from the problem of core language learning,  we conduct all these studies using synthetic languages that are grounded in simple visual environments.   In the first article, we study how to train agents to follow compositional instructions in environments with a restricted form of supervision. Namely for every instruction and initial environment configuration we only provide a goal-state instead of a complete trajectory with actions at all steps. We adapt adversarial imitation learning methods to this setting and demonstrate that such a restricted form of data is sufficient to learn compositional meanings of the instructions. Our second article also focuses on instruction following. We develop the BabyAI platform to facilitate further, more extensive and rigorous studies of this setup. The platform features a compositional Baby language with $10^{19}$ instructions, whose semantics is precisely defined in a partially-observable gridworld environment. We report baseline results on how much supervision is required to teach the agent certain subsets of Baby language with different training methods, such as reinforcement learning and imitation learning.   In the third article we study systematic generalization of visual question answering (VQA) models. In the VQA setting the system must answer compositional questions about images. We construct a dataset of spatial questions about object pairs and evaluate how well different models perform on questions about pairs of objects that never occured in the same question in the training distribution. We show that models in which word meanings are represented by separate modules that perform independent computation generalize much better than models whose design is not explicitly modular. The modular models, however,  generalize well only when the modules are connected in an appropriate layout, and our experiments highlight the challenges of learning the layout by end-to-end learning on the training distribution. In our fourth and final article we also study generalization of VQA models to questions outside of the training distribution, but this time using the popular CLEVR dataset of complex questions about 3D-rendered scenes as the platform. We generate novel CLEVR-like questions by using similarity-based references (e.g. ``the ball that has the same color as ...'') in contexts that occur in CLEVR questions but only with location-based references (e.g. ``the ball that is to the left of ...'').  We analyze zero- and few- shot generalization to CLOSURE after training on CLEVR for a number of existing models as well as a novel one.

Keyphrases: Grounded Language Learning, Deep Learning, Systematic Generalization, Sample Efficiency, Instruction Following, Visual Question Answering