Generalization in Generation: A closer look at Exposure Bias
This work addresses a fundamental problem in generative modeling for researchers, offering a novel approach to improve generalization, though it appears incremental in its methodological contributions.
The paper investigates exposure bias in autoregressive generative models, arguing that generalization is the underlying issue and proposing unconditional generation as a benchmark. It introduces a method combining latent variable modeling with reinforcement learning exploration, showing improved generalization in language modeling and variational sentence auto-encoding tasks.
Exposure bias refers to the train-test discrepancy that seemingly arises when an autoregressive generative model uses only ground-truth contexts at training time but generated ones at test time. We separate the contributions of the model and the learning framework to clarify the debate on consequences and review proposed counter-measures. In this light, we argue that generalization is the underlying property to address and propose unconditional generation as its fundamental benchmark. Finally, we combine latent variable modeling with a recent formulation of exploration in reinforcement learning to obtain a rigorous handling of true and generated contexts. Results on language modeling and variational sentence auto-encoding confirm the model's generalization capability.