Can Machines Learn Common-Sense Reasoning?

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Common-sense reasoning (CSR) has recently garnered a significant renewal of interest in the Artificial Intelligence community, resulting from the advent of certain technologies (e.g., dialogue systems, story understanding software, and recommendation tools) which can seem strikingly unintelligent in the absence of common sense. The goal of the proposed research is to make the learning of common-sense reasoning achievable for Machine Learning (ML) models, in order to help them generalize to complex scenarios that are encountered frequently in  the real world. Specifically, the research will first attempt to quantify the prevalence and classify the complexities of common-sense demanding problems in a variety of natural language understanding applications. The research will next proceed with studying strategies for the development of evaluation protocols that precisely identify how deep learning models make use of train instances to resolve problems at test time. When coupled with the first step of the research plan, these evaluation protocols will be able to provide us with a more nuanced understanding of the difficulty of instances for models. In addition, the research will explore incorporating in these protocols corrective measures for gender, racial and other forms of bias present in today's language generation models. Inspired by principles in software engineering, the use of all-purpose behavioural measures, such as invariance or minimum functionality tests, will also be explored and promoted as an important new benchmark measure for the current and future NLP systems. The next step of research will be dedicated to the development of novel deep learning models, based on the recently proposed Transformer architecture, which are potentially CSR-endowed, through modifying and augmenting various model components, including the attention mechanism, the decoder/encoder layers, and the training method. Some of the fundamental and technological questions that the research will address include: i) How can we develop a unified, generalized system that is capable of simultaneously tackling a number of difficult CSR tasks, as well as their more general, downstream counterparts with high coverage and interpretability; ii) How can we determine whether an improved performance on various Natural Language Processing (NLP) benchmarks represents a genuine progress towards common-sense-enabled systems; iii) How can we devise benchmarks that are more challenging, realistic and large-scale; iv) How can we use common-sense reasoning to help bridge the performance gap between robots and humans in the real world; and v) Can common-sense reasoning be used as a basis for developing end-to-end debiasing techniques that can combat various forms of biases prevalent in current pre-trained language models and corpora (gender/race/cultural)?