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In this work, we develop a novel regularizer to improve the learning oflong-range dependency of sequence data. Applied on language modelling, ourregularizer expresses the inductive bias that sequence variables should havehigh mutual information even though the model might not see abundantobservations for complex long-range dependency. We show how the `next sentenceprediction (classification)' heuristic can be derived in a principled way fromour mutual information estimation framework, and be further extended tomaximize the mutual information of sequence variables. The proposed approachnot only is effective at increasing the mutual information of segments underthe learned model but more importantly, leads to a higher likelihood on holdoutdata, and improved generation quality. Code is released athttps://github.com/BorealisAI/BMI.

Better Long-Range Dependency By Bootstrapping A Mutual Information Regularizer