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Autoregressive generative models are commonly used, especially for thosetasks involving sequential data. They have, however, been plagued by a slew ofinherent flaws due to the intrinsic characteristics of chain-style conditionalmodeling (e.g., exposure bias or lack of long-range coherence), severelylimiting their ability to model distributions properly. In this paper, wepropose a unique method termed E-ARM for training autoregressive generativemodels that takes advantage of a well-designed energy-based learning objective.By leveraging the extra degree of freedom of the softmax operation, we areallowed to make the autoregressive model itself be an energy-based model formeasuring the likelihood of input without introducing any extra parameters.Furthermore, we show that E-ARM can be trained efficiently and is capable ofalleviating the exposure bias problem and increase temporal coherence forautoregressive generative models. Extensive empirical results, coveringbenchmarks like language modeling, neural machine translation, and imagegeneration, demonstrate the effectiveness of the proposed approach.

Your Autoregressive Generative Model Can be Better If You Treat It as an Energy-Based One