Chong Li


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A Comprehensive Neural and Behavioral Task Taxonomy Method for Transfer Learning in NLP
Yunhao Zhang | Chong Li | Xiaohan Zhang | Xinyi Dong | Shaonan Wang
Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023 (Findings)

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Interpreting and Exploiting Functional Specialization in Multi-Head Attention under Multi-task Learning
Chong Li | Shaonan Wang | Yunhao Zhang | Jiajun Zhang | Chengqing Zong
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Transformer-based models, even though achieving super-human performance on several downstream tasks, are often regarded as a black box and used as a whole. It is still unclear what mechanisms they have learned, especially their core module: multi-head attention. Inspired by functional specialization in the human brain, which helps to efficiently handle multiple tasks, this work attempts to figure out whether the multi-head attention module will evolve similar function separation under multi-tasking training. If it is, can this mechanism further improve the model performance? To investigate these questions, we introduce an interpreting method to quantify the degree of functional specialization in multi-head attention. We further propose a simple multi-task training method to increase functional specialization and mitigate negative information transfer in multi-task learning. Experimental results on seven pre-trained transformer models have demonstrated that multi-head attention does evolve functional specialization phenomenon after multi-task training which is affected by the similarity of tasks. Moreover, the multi-task training strategy based on functional specialization boosts performance in both multi-task learning and transfer learning without adding any parameters.


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Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models
Chong Li | Cenyuan Zhang | Xiaoqing Zheng | Xuanjing Huang
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

A sequence-to-sequence learning with neural networks has empirically proven to be an effective framework for Chinese Spelling Correction (CSC), which takes a sentence with some spelling errors as input and outputs the corrected one. However, CSC models may fail to correct spelling errors covered by the confusion sets, and also will encounter unseen ones. We propose a method, which continually identifies the weak spots of a model to generate more valuable training instances, and apply a task-specific pre-training strategy to enhance the model. The generated adversarial examples are gradually added to the training set. Experimental results show that such an adversarial training method combined with the pre-training strategy can improve both the generalization and robustness of multiple CSC models across three different datasets, achieving state-of-the-art performance for CSC task.