latent-GLAT: Glancing at Latent Variables for Parallel Text Generation

Recently, parallel text generation has received widespread attention due to its success in generation efficiency. Although many advanced techniques are proposed to improve its generation quality, they still need the help of an autoregressive model for training to overcome the one-to-many multi-modal phenomenon in the dataset, limiting their applications. In this paper, we propose latent-GLAT, which employs the discrete latent variables to capture word categorical information and invoke an advanced curriculum learning technique, alleviating the multi-modality problem. Experiment results show that our method outperforms strong baselines without the help of an autoregressive model, which further broadens the application scenarios of the parallel decoding paradigm. (double dagger)

Automated summary

This paper introduces a method called latent-GLAT, which uses discrete latent variables to capture word categorical information and employs curriculum learning technique to alleviate the multi-modality problem. Experimental results show that this method outperforms strong baselines without the help of an autoregressive model, further broadening the application scenarios of the parallel decoding paradigm.