Title: YOUR AUTOREGRESSIVE GENERATIVE MODEL CAN BE BETTER IF YOU TREAT IT AS AN ENERGY-BASED ONE. Abstract: Autoregressive generative models are commonly used, especially for those tasks involving sequential data. They have, however, been plagued by a slew of inherent flaws due to the intrinsic characteristics of chain-style conditional modeling (e.g., exposure bias or lack of long-range coherence), severely limiting their ability to model distributions properly. In this paper, we propose a unique method for training the autoregressive generative model that takes advantage of a well-designed energy-based learning objective. We show that our method is capable of alleviating the exposure bias problem and increase temporal coherence by imposing a constraint which fits joint distributions at each time step. Besides, unlike former energy-based models, we estimate energy scores based on the underlying autoregressive network itself, which does not require any extra network. Finally, thanks to importance sampling, we can train the entire model efficiently without requiring an MCMC process. Extensive empirical results, covering benchmarks like language modeling, neural machine translation, and image generation, demonstrate the effectiveness of the proposed approach.