Yiding Tan


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Template-free Prompt Tuning for Few-shot NER
Ruotian Ma | Xin Zhou | Tao Gui | Yiding Tan | Linyang Li | Qi Zhang | Xuanjing Huang
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Prompt-based methods have been successfully applied in sentence-level few-shot learning tasks, mostly owing to the sophisticated design of templates and label words. However, when applied to token-level labeling tasks such as NER, it would be time-consuming to enumerate the template queries over all potential entity spans. In this work, we propose a more elegant method to reformulate NER tasks as LM problems without any templates. Specifically, we discard the template construction process while maintaining the word prediction paradigm of pre-training models to predict a class-related pivot word (or label word) at the entity position. Meanwhile, we also explore principled ways to automatically search for appropriate label words that the pre-trained models can easily adapt to. While avoiding the complicated template-based process, the proposed LM objective also reduces the gap between different objectives used in pre-training and fine-tuning, thus it can better benefit the few-shot performance. Experimental results demonstrate the effectiveness of the proposed method over bert-tagger and template-based method under few-shot settings. Moreover, the decoding speed of the proposed method is up to 1930.12 times faster than the template-based method.


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TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing
Xiao Wang | Qin Liu | Tao Gui | Qi Zhang | Yicheng Zou | Xin Zhou | Jiacheng Ye | Yongxin Zhang | Rui Zheng | Zexiong Pang | Qinzhuo Wu | Zhengyan Li | Chong Zhang | Ruotian Ma | Zichu Fei | Ruijian Cai | Jun Zhao | Xingwu Hu | Zhiheng Yan | Yiding Tan | Yuan Hu | Qiyuan Bian | Zhihua Liu | Shan Qin | Bolin Zhu | Xiaoyu Xing | Jinlan Fu | Yue Zhang | Minlong Peng | Xiaoqing Zheng | Yaqian Zhou | Zhongyu Wei | Xipeng Qiu | Xuanjing Huang
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations

TextFlint is a multilingual robustness evaluation toolkit for NLP tasks that incorporates universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses. This enables practitioners to automatically evaluate their models from various aspects or to customize their evaluations as desired with just a few lines of code. TextFlint also generates complete analytical reports as well as targeted augmented data to address the shortcomings of the model in terms of its robustness. To guarantee acceptability, all the text transformations are linguistically based and all the transformed data selected (up to 100,000 texts) scored highly under human evaluation. To validate the utility, we performed large-scale empirical evaluations (over 67,000) on state-of-the-art deep learning models, classic supervised methods, and real-world systems. The toolkit is already available at https://github.com/textflint with all the evaluation results demonstrated at textflint.io.