Chong Zhang


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Hierarchical Information Matters: Text Classification via Tree Based Graph Neural Network
Chong Zhang | He Zhu | Xingyu Peng | Junran Wu | Ke Xu
Proceedings of the 29th International Conference on Computational Linguistics

Text classification is a primary task in natural language processing (NLP). Recently, graph neural networks (GNNs) have developed rapidly and been applied to text classification tasks. As a special kind of graph data, the tree has a simpler data structure and can provide rich hierarchical information for text classification. Inspired by the structural entropy, we construct the coding tree of the graph by minimizing the structural entropy and propose HINT, which aims to make full use of the hierarchical information contained in the text for the task of text classification. Specifically, we first establish a dependency parsing graph for each text. Then we designed a structural entropy minimization algorithm to decode the key information in the graph and convert each graph to its corresponding coding tree. Based on the hierarchical structure of the coding tree, the representation of the entire graph is obtained by updating the representation of non-leaf nodes in the coding tree layer by layer. Finally, we present the effectiveness of hierarchical information in text classification. Experimental results show that HINT outperforms the state-of-the-art methods on popular benchmarks while having a simple structure and few parameters.


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A Partition Filter Network for Joint Entity and Relation Extraction
Zhiheng Yan | Chong Zhang | Jinlan Fu | Qi Zhang | Zhongyu Wei
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

In joint entity and relation extraction, existing work either sequentially encode task-specific features, leading to an imbalance in inter-task feature interaction where features extracted later have no direct contact with those that come first. Or they encode entity features and relation features in a parallel manner, meaning that feature representation learning for each task is largely independent of each other except for input sharing. We propose a partition filter network to model two-way interaction between tasks properly, where feature encoding is decomposed into two steps: partition and filter. In our encoder, we leverage two gates: entity and relation gate, to segment neurons into two task partitions and one shared partition. The shared partition represents inter-task information valuable to both tasks and is evenly shared across two tasks to ensure proper two-way interaction. The task partitions represent intra-task information and are formed through concerted efforts of both gates, making sure that encoding of task-specific features is dependent upon each other. Experiment results on six public datasets show that our model performs significantly better than previous approaches. In addition, contrary to what previous work has claimed, our auxiliary experiments suggest that relation prediction is contributory to named entity prediction in a non-negligible way. The source code can be found at

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Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training
Zhengyan Li | Yicheng Zou | Chong Zhang | Qi Zhang | Zhongyu Wei
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Aspect-based sentiment analysis aims to identify the sentiment polarity of a specific aspect in product reviews. We notice that about 30% of reviews do not contain obvious opinion words, but still convey clear human-aware sentiment orientation, which is known as implicit sentiment. However, recent neural network-based approaches paid little attention to implicit sentiment entailed in the reviews. To overcome this issue, we adopt Supervised Contrastive Pre-training on large-scale sentiment-annotated corpora retrieved from in-domain language resources. By aligning the representation of implicit sentiment expressions to those with the same sentiment label, the pre-training process leads to better capture of both implicit and explicit sentiment orientation towards aspects in reviews. Experimental results show that our method achieves state-of-the-art performance on SemEval2014 benchmarks, and comprehensive analysis validates its effectiveness on learning implicit sentiment.

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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 with all the evaluation results demonstrated at

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Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation
Chong Zhang | Jieyu Zhao | Huan Zhang | Kai-Wei Chang | Cho-Jui Hsieh
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Robustness and counterfactual bias are usually evaluated on a test dataset. However, are these evaluations robust? If the test dataset is perturbed slightly, will the evaluation results keep the same? In this paper, we propose a “double perturbation” framework to uncover model weaknesses beyond the test dataset. The framework first perturbs the test dataset to construct abundant natural sentences similar to the test data, and then diagnoses the prediction change regarding a single-word substitution. We apply this framework to study two perturbation-based approaches that are used to analyze models’ robustness and counterfactual bias in English. (1) For robustness, we focus on synonym substitutions and identify vulnerable examples where prediction can be altered. Our proposed attack attains high success rates (96.0%-99.8%) in finding vulnerable examples on both original and robustly trained CNNs and Transformers. (2) For counterfactual bias, we focus on substituting demographic tokens (e.g., gender, race) and measure the shift of the expected prediction among constructed sentences. Our method is able to reveal the hidden model biases not directly shown in the test dataset. Our code is available at


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Extracting Kinship from Obituary to Enhance Electronic Health Records for Genetic Research
Kai He | Jialun Wu | Xiaoyong Ma | Chong Zhang | Ming Huang | Chen Li | Lixia Yao
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task

Claims database and electronic health records database do not usually capture kinship or family relationship information, which is imperative for genetic research. We identify online obituaries as a new data source and propose a special named entity recognition and relation extraction solution to extract names and kinships from online obituaries. Built on 1,809 annotated obituaries and a novel tagging scheme, our joint neural model achieved macro-averaged precision, recall and F measure of 72.69%, 78.54% and 74.93%, and micro-averaged precision, recall and F measure of 95.74%, 98.25% and 96.98% using 57 kinships with 10 or more examples in a 10-fold cross-validation experiment. The model performance improved dramatically when trained with 34 kinships with 50 or more examples. Leveraging additional information such as age, death date, birth date and residence mentioned by obituaries, we foresee a promising future of supplementing EHR databases with comprehensive and accurate kinship information for genetic research.


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A Case Study of Machine Translation in Financial Sentiment Analysis
Chong Zhang | Matteo Capelletti | Alexandros Poulis | Thorben Stemann | Jane Nemcova
Proceedings of Machine Translation Summit XVI: Commercial MT Users and Translators Track


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A Refined Notion of Memory Usage for Minimalist Parsing
Thomas Graf | Brigitta Fodor | James Monette | Gianpaul Rachiele | Aunika Warren | Chong Zhang
Proceedings of the 14th Meeting on the Mathematics of Language (MoL 2015)