Tailoring Language Generation Models under Total Variation Distance

Haozhe Ji, Pei Ke, Zhipeng Hu, Rongsheng Zhang, Minlie Huang

Keywords: language generation, maximum likelihood estimation, total variation distance, text degeneration

TL;DR: We analyze total variation distance (TVD) as a robust metric to outliers and devise a new training objective based on TVD to alleviate text degeneration and improve the generation quality.

Abstract: The standard paradigm of neural language generation adopts maximum likelihood estimation (MLE) as the optimizing method. From a distributional view, MLE in fact minimizes the Kullback-Leibler divergence (KLD) between the distribution of the real data and that of the model. However, this approach forces the model to distribute non-zero (sometimes large) probability mass to all training samples regardless of their quality. Moreover, in the attempt to cover the low-probability regions in the data distribution, the model systematically overestimates the probability of corrupted text sequences, which we conjecture is one of the main reasons for text degeneration during autoregressive decoding. To remedy this problem, we leverage the total variation distance (TVD) with its robustness to outliers, and develop practical bounds to apply it to language generation. Then, we introduce the TaiLr objective that balances the tradeoff of estimating TVD. Intuitively, TaiLr downweights real data samples that have low model probabilities with tunable penalization intensity. Experimental results show that our method alleviates the overestimation of degenerated sequences without sacrificing diversity and improves generation quality on a wide range of text generation tasks.

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