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Despite recent advances in neural text generation, encoding the richdiversity in human language remains elusive. We argue that the sub-optimal textgeneration is mainly attributable to the imbalanced token distribution, whichparticularly misdirects the learning model when trained with themaximum-likelihood objective. As a simple yet effective remedy, we propose twonovel methods, F^2-Softmax and MefMax, for a balanced training even with theskewed frequency distribution. MefMax assigns tokens uniquely to frequencyclasses, trying to group tokens with similar frequencies and equalize frequencymass between the classes. F^2-Softmax then decomposes a probabilitydistribution of the target token into a product of two conditionalprobabilities of (i) frequency class, and (ii) token from the target frequencyclass. Models learn more uniform probability distributions because they areconfined to subsets of vocabularies. Significant performance gains on sevenrelevant metrics suggest the supremacy of our approach in improving not onlythe diversity but also the quality of generated texts.

F^2-Softmax: Diversifying Neural Text Generation via Frequency Factorized Softmax