Yongxin Zhang


2023

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Actively Supervised Clustering for Open Relation Extraction
Jun Zhao | Yongxin Zhang | Qi Zhang | Tao Gui | Zhongyu Wei | Minlong Peng | Mingming Sun
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Current clustering-based Open Relation Extraction (OpenRE) methods usually adopt a two-stage pipeline, which simultaneously learns relation representations and assignments in the first stage, then manually labels relation for each cluster. However, unsupervised objectives struggle to explicitly optimize clusters to align with relational semantics, and the number of clusters K has to be supplied in advance. In this paper, we present a novel setting, named actively supervised clustering for OpenRE. Our insight lies in that clustering learning and relation labeling can be performed simultaneously, which provides the necessary guidance for clustering without a significant increase in human effort. Along with this setting, we propose an active labeling strategy tailored for clustering. Instead of only focusing on improving the clustering of relations that have been discovered, our strategy is encouraged to discover new relations through diversity regularization. This is particularly beneficial for long-tail relations in the real world. Experimental results show that our method is able to discover almost all relational clusters in the data and improve the SOTA methods by 13.8% and 10.6%, on two datasets respectively.

2021

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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.