Ningyuan Sun


2023

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TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities
Zhe Zhao | Yudong Li | Cheng Hou | Jing Zhao | Rong Tian | Weijie Liu | Yiren Chen | Ningyuan Sun | Haoyan Liu | Weiquan Mao | Han Guo | Weigang Gou | Taiqiang Wu | Tao Zhu | Wenhang Shi | Chen Chen | Shan Huang | Sihong Chen | Liqun Liu | Feifei Li | Xiaoshuai Chen | Xingwu Sun | Zhanhui Kang | Xiaoyong Du | Linlin Shen | Kimmo Yan
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)

Recently, the success of pre-training in text domain has been fully extended to vision, audio, and cross-modal scenarios. The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework. In this paper, we present TencentPretrain, a toolkit supporting pre-training models of different modalities. The core feature of TencentPretrain is the modular design. The toolkit uniformly divides pre-training models into 5 components: embedding, encoder, target embedding, decoder, and target. As almost all of common modules are provided in each component, users can choose the desired modules from different components to build a complete pre-training model. The modular design enables users to efficiently reproduce existing pre-training models or build brand-new one. We test the toolkit on text, vision, and audio benchmarks and show that it can match the performance of the original implementations.

2022

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Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching
Kunbo Ding | Weijie Liu | Yuejian Fang | Zhe Zhao | Qi Ju | Xuefeng Yang | Rong Tian | Zhu Tao | Haoyan Liu | Han Guo | Xingyu Bai | Weiquan Mao | Yudong Li | Weigang Guo | Taiqiang Wu | Ningyuan Sun
Findings of the Association for Computational Linguistics: NAACL 2022

Previous studies have proved that cross-lingual knowledge distillation can significantly improve the performance of pre-trained models for cross-lingual similarity matching tasks. However, the student model needs to be large in this operation. Otherwise, its performance will drop sharply, thus making it impractical to be deployed to memory-limited devices. To address this issue, we delve into cross-lingual knowledge distillation and propose a multi-stage distillation framework for constructing a small-size but high-performance cross-lingual model. In our framework, contrastive learning, bottleneck, and parameter recurrent strategies are delicately combined to prevent performance from being compromised during the compression process. The experimental results demonstrate that our method can compress the size of XLM-R and MiniLM by more than 50%, while the performance is only reduced by about 1%.