Zhichun Guo


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A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods
Zhihan Zhang | Wenhao Yu | Mengxia Yu | Zhichun Guo | Meng Jiang
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Multi-task learning (MTL) has become increasingly popular in natural language processing (NLP) because it improves the performance of related tasks by exploiting their commonalities and differences. Nevertheless, it is still not understood very well how multi-task learning can be implemented based on the relatedness of training tasks. In this survey, we review recent advances of multi-task learning methods in NLP, with the aim of summarizing them into two general multi-task training methods based on their task relatedness: (i) joint training and (ii) multi-step training. We present examples in various NLP downstream applications, summarize the task relationships and discuss future directions of this promising topic.


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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.