latent-GLAT: Glancing at Latent Variables for Parallel Text Generation
Yu Bao, Hao Zhou, Shujian Huang, Dongqi Wang, Lihua Qian, Xinyu Dai, Jiajun Chen, Lei Li
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 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.
BibTeX
@inproceedings{bao-etal-2022-textit,
title = "{latent-GLAT}: Glancing at Latent Variables for Parallel Text Generation",
author = "Bao, Yu and
Zhou, Hao and
Huang, Shujian and
Wang, Dongqi and
Qian, Lihua and
Dai, Xinyu and
Chen, Jiajun and
Li, Lei",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.575/",
doi = "10.18653/v1/2022.acl-long.575",
pages = "8398--8409"
}