Zhichun Guo


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Sentence-Permuted Paragraph Generation
Wenhao Yu | Chenguang Zhu | Tong Zhao | Zhichun Guo | Meng Jiang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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, and decoding in the sentence-permuted generation. Experiments on three paragraph generation benchmarks demonstrate PermGen generates more diverse outputs with a higher quality than existing models.