$\textit{latent}$-GLAT: Glancing at Latent Variables for Parallel Text Generation

• URL: http://arxiv.org/abs/2204.02030v1
• Date: Tue, 5 Apr 2022 07:34:12 GMT
• Title: $\textit{latent}$-GLAT: Glancing at Latent Variables for Parallel Text Generation
• Authors: Yu Bao, Hao Zhou, Shujian Huang, Dongqi Wang, Lihua Qian, Xinyu Dai, Jiajun Chen and Lei Li
• Abstract summary: parallel text generation has received widespread attention due to its success in generation efficiency. In this paper, we propose $textitlatent$-GLAT, which employs the discrete latent variables to capture word categorical information. Experiment results show that our method outperforms strong baselines without the help of an autoregressive model.
• Score: 65.29170569821093
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 $\textit{latent}$-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.

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