Linlin Shen


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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.


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CSL: A Large-scale Chinese Scientific Literature Dataset
Yudong Li | Yuqing Zhang | Zhe Zhao | Linlin Shen | Weijie Liu | Weiquan Mao | Hui Zhang
Proceedings of the 29th International Conference on Computational Linguistics

Scientific literature serves as a high-quality corpus, supporting a lot of Natural Language Processing (NLP) research. However, existing datasets are centered around the English language, which restricts the development of Chinese scientific NLP. In this work, we present CSL, a large-scale Chinese Scientific Literature dataset, which contains the titles, abstracts, keywords and academic fields of 396k papers. To our knowledge, CSL is the first scientific document dataset in Chinese. The CSL can serve as a Chinese corpus. Also, this semi-structured data is a natural annotation that can constitute many supervised NLP tasks. Based on CSL, we present a benchmark to evaluate the performance of models across scientific domain tasks, i.e., summarization, keyword generation and text classification. We analyze the behavior of existing text-to-text models on the evaluation tasks and reveal the challenges for Chinese scientific NLP tasks, which provides a valuable reference for future research. Data and code will be publicly available.