Xiao Wang


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MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective
Xiao Wang | Shihan Dou | Limao Xiong | Yicheng Zou | Qi Zhang | Tao Gui | Liang Qiao | Zhanzhan Cheng | Xuanjing Huang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

NER model has achieved promising performance on standard NER benchmarks. However, recent studies show that previous approaches may over-rely on entity mention information, resulting in poor performance on out-of-vocabulary(OOV) entity recognition. In this work, we propose MINER, a novel NER learning framework, to remedy this issue from an information-theoretic perspective. The proposed approach contains two mutual information based training objectives: i) generalizing information maximization, which enhances representation via deep understanding of context and entity surface forms; ii) superfluous information minimization, which discourages representation from rotate memorizing entity names or exploiting biased cues in data. Experiments on various settings and datasets demonstrate that it achieves better performance in predicting OOV entities.


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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.


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Cross-Lingual Document Retrieval with Smooth Learning
Jiapeng Liu | Xiao Zhang | Dan Goldwasser | Xiao Wang
Proceedings of the 28th International Conference on Computational Linguistics

Cross-lingual document search is an information retrieval task in which the queries’ language and the documents’ language are different. In this paper, we study the instability of neural document search models and propose a novel end-to-end robust framework that achieves improved performance in cross-lingual search with different documents’ languages. This framework includes a novel measure of the relevance, smooth cosine similarity, between queries and documents, and a novel loss function, Smooth Ordinal Search Loss, as the objective function. We further provide theoretical guarantee on the generalization error bound for the proposed framework. We conduct experiments to compare our approach with other document search models, and observe significant gains under commonly used ranking metrics on the cross-lingual document retrieval task in a variety of languages.