Fˆ2-Softmax: Diversifying Neural Text Generation via Frequency Factorized Softmax (2020)

Abstract: Despite recent advances in neural text generation, encoding the rich diversity in human language remains elusive. We argue that the sub-optimal text generation is mainly attributable to the imbalanced token distribution, which particularly misdirects the learning model when trained with the maximum-likelihood objective. As a simple yet effective remedy, we propose two novel methods, Fˆ2-Softmax and MefMax, for a balanced training even with the skewed frequency distribution. MefMax assigns tokens uniquely to frequency classes, trying to group tokens with similar frequencies and equalize frequency mass between the classes. Fˆ2-Softmax then decomposes a probability distribution of the target token into a product of two conditional probabilities of (1) frequency class, and (2) token from the target frequency class. Models learn more uniform probability distributions because they are confined to subsets of vocabularies. Significant performance gains on seven relevant metrics suggest the supremacy of our approach in improving not only the diversity but also the quality of generated texts.

## introduction neural text generation is one of the extensively studied tasks of natural language processing (nlp), as it forms the basis for dialogue systems @xcite , machine translation @xcite , and text summarization @xcite . however, often monotonous or dull, texts generated from existing methods do not fully reflect the rich diversity and expression in human language @xcite . in particular, models tend to overproduce words frequently appearing in the data, while hardly utilizing informative words @xcite . even pre-training techniques on large corpora fail to resolve the issue @xcite . possible causes for text degeneration have been illuminated, such as a defect specific to model architectures @xcite or the discrepancy between training data and a true distribution @xcite . recently, the emphasis has been placed on investigating the flaws in the maximum likelihood objective @xcite . concretely, the likelihood training pays little attention to the top ranks in terms of the target token probabilities @xcite , or maximizing likelihood itself does not adequately reflect human language processing @xcite . therefore, with the maximum likelihoodbased training, models learn to produce tokens frequently appearing in the data more often. we argue, however, that the primary reason behind the sub-optimal performance of the likelihood objective is essentially the imbalanced token distribution inherent in natural language. natural language is extremely skewed in distribution, where the top hundred most frequently-used (top-100) words occupy nearly half of the total corpus @xcite following the zipf's law @xcite . training a classifier with the inherently imbalanced data on the maximum likelihood estimation (mle) leads to biased classification boundaries in favor of majority classes @xcite . in other words, models play a difficult role in learning with the imbalanced label (i.e., token) distribution @xcite . we hypothesize that text generation can be enriched by balancing out the training data distribution. to this end, we introduce f 2 -softmax (fig. 1(b ), section 3.2), which factorizes the probability distribution of the target token into a product of two conditional probabilities of (i) frequency class, and (ii) token from the target frequency class. it ensures training over balanced data, since the frequency classes are designed to have the distribution close to uniformity, and token distributions within a class are confined to subsets of vocabularies grouped with similar frequencies. to this end, all unique tokens are assigned to a frequency class prior to the training, by our novel mean efficiency maximization (mefmax; fig. 1(a ), section 3.3). mefmax evaluates and maximizes the class-labeling performance with the normalized entropy (i.e., efficiency), having the probability distributions to be learned as uniform as possible. we conduct extensive performance evaluations on seven relevant metrics that quantify the diversity and quality of generated texts. in terms of the diversity of generated texts, our approach significantly outperforms not only the mle baseline @xcite but also other diversity-promoting alternatives @xcite . we also achieve state-of-the-art results on most of the quality performances. ## conclusion in this paper, we proposed f 2 -softmax, a simple but effective method for better learning the rich diversity in text. f 2 -softmax encourages models to diversify text generation by readjusting class formation and motivating models to learn a more balanced token distribution. quantitative and qualitative analyses validate the diversity-promoting performances of our approach. since it can be quickly adopted to replace the traditional likelihood objective, we believe in broader applicability of f 2 -softmax. thus, future work involves extending the method to other related tasks, such as machine translation and text summarization, and investigating the potential gains from transfer learning.