@article{yu2021sentence-permuted,
author = {Yu, Wenhao and Zhu, Chenguang and Zhao, Tong and Guo, Zhichun and Jiang, Meng},
title = {Sentence-Permuted Paragraph Generation},
year = {2021},
month = {April},
abstract = {Generating paragraphs of diverse contents is important in many applications. Existing generation models produce similar contents from homogenized contexts due to the fixed left-to-right sentence order. Our idea is permuting the sentence orders to improve the content diversity of multi-sentence paragraph. We propose a novel framework PermGen whose objective is to maximize the expected log-likelihood of output paragraph distributions with respect to all possible sentence orders. PermGen uses hierarchical positional embedding and designs new procedures for training, decoding, and candidate ranking in the sentence-permuted generation. Experiments on three paragraph generation benchmarks demonstrate PermGen generates more diverse outputs with a higher quality than existing models.},
url = {http://approjects.co.za/?big=en-us/research/publication/sentence-permuted-paragraph-generation/},
journal = {Empirical Methods in Natural Language Processing (EMNLP) 2021},
}