How do they interpret the model?
They find relations that connect questions to the answer-options.
Computational context understanding refers to an agent's ability to fuse disparate sources of information for decision-making and is, therefore, generally regarded as a prerequisite for sophisticated machine reasoning capabilities, such as in artificial intelligence (AI). Data-driven and knowledge-driven methods are two classical techniques in the pursuit of such machine sense-making capability. However, while data-driven methods seek to model the statistical regularities of events by making observations in the real-world, they remain difficult to interpret and they lack mechanisms for naturally incorporating external knowledge. Conversely, knowledge-driven methods, combine structured knowledge bases, perform symbolic reasoning based on axiomatic principles, and are more interpretable in their inferential processing; however, they often lack the ability to estimate the statistical salience of an inference. To combat these issues, we propose the use of hybrid AI methodology as a general framework for combining the strengths of both approaches. Specifically, we inherit the concept of neuro-symbolism as a way of using knowledge-bases to guide the learning progress of deep neural networks. We further ground our discussion in two applications of neuro-symbolism and, in both cases, show that our systems maintain interpretability while achieving comparable performance, relative to the state-of-the-art.
Context understanding is a natural property of human cognition, that supports our decision-making capabilities in complex sensory environments. Humans are capable of fusing information from a variety of modalities|e.g., auditory, visual|in order to perform different tasks, ranging from the operation of a motor vehicle to the generation of logical inferences based on commonsense. Allen Newell and Herbert Simon described this sense-making capability in their theory of cognition BIBREF0, BIBREF1: through sensory stimuli, humans accumulate experiences, generalize, and reason over them, storing the resulting knowledge in memory; the dynamic combination of live experience and distilled knowledge during task-execution, enables humans to make time-effective decisions and evaluate how good or bad a decision was by factoring in external feedback. Endowing machines with this sense-making capability has been one of the long-standing goals of Artificial Intelligence (AI) practice and research, both in industry and academia. Data-driven and knowledge-driven methods are two classical techniques in the pursuit of such machine sense-making capability. Sense-making is not only a key for improving machine autonomy, but is a precondition for enabling seamless interaction with humans. Humans communicate effectively with each other, thanks to their shared mental models of the physical world and social context BIBREF2. These models foster reciprocal trust by making contextual knowledge transparent; they are also crucial for explaining how decision-making unfolds. In a similar fashion, we can assert that `explainable AI' is a byproduct or an affordance of computational context understanding and is predicated on the extent to which humans can introspect the decision processes that enable machine sense-making BIBREF3.