Huiwei Zhou

Also published as: HuiWei Zhou


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Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction
Huiwei Zhou | Yibin Xu | Weihong Yao | Zhe Liu | Chengkun Lang | Haibin Jiang
Proceedings of the 28th International Conference on Computational Linguistics

Document-level Relation Extraction (RE) is particularly challenging due to complex semantic interactions among multiple entities in a document. Among exiting approaches, Graph Convolutional Networks (GCN) is one of the most effective approaches for document-level RE. However, traditional GCN simply takes word nodes and adjacency matrix to represent graphs, which is difficult to establish direct connections between distant entity pairs. In this paper, we propose Global Context-enhanced Graph Convolutional Networks (GCGCN), a novel model which is composed of entities as nodes and context of entity pairs as edges between nodes to capture rich global context information of entities in a document. Two hierarchical blocks, Context-aware Attention Guided Graph Convolution (CAGGC) for partially connected graphs and Multi-head Attention Guided Graph Convolution (MAGGC) for fully connected graphs, could take progressively more global context into account. Meantime, we leverage a large-scale distantly supervised dataset to pre-train a GCGCN model with curriculum learning, which is then fine-tuned on the human-annotated dataset for further improving document-level RE performance. The experimental results on DocRED show that our model could effectively capture rich global context information in the document, leading to a state-of-the-art result. Our code is available at


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DUT-NLP at MEDIQA 2019: An Adversarial Multi-Task Network to Jointly Model Recognizing Question Entailment and Question Answering
Huiwei Zhou | Xuefei Li | Weihong Yao | Chengkun Lang | Shixian Ning
Proceedings of the 18th BioNLP Workshop and Shared Task

In this paper, we propose a novel model called Adversarial Multi-Task Network (AMTN) for jointly modeling Recognizing Question Entailment (RQE) and medical Question Answering (QA) tasks. AMTN utilizes a pre-trained BioBERT model and an Interactive Transformer to learn the shared semantic representations across different task through parameter sharing mechanism. Meanwhile, an adversarial training strategy is introduced to separate the private features of each task from the shared representations. Experiments on BioNLP 2019 RQE and QA Shared Task datasets show that our model benefits from the shared representations of both tasks provided by multi-task learning and adversarial training, and obtains significant improvements upon the single-task models.

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DUT-BIM at MEDIQA 2019: Utilizing Transformer Network and Medical Domain-Specific Contextualized Representations for Question Answering
Huiwei Zhou | Bizun Lei | Zhe Liu | Zhuang Liu
Proceedings of the 18th BioNLP Workshop and Shared Task

In medical domain, given a medical question, it is difficult to manually select the most relevant information from a large number of search results. BioNLP 2019 proposes Question Answering (QA) task, which encourages the use of text mining technology to automatically judge whether a search result is an answer to the medical question. The main challenge of QA task is how to mine the semantic relation between question and answer. We propose BioBERT Transformer model to tackle this challenge, which applies Transformers to extract semantic relation between different words in questions and answers. Furthermore, BioBERT is utilized to encode medical domain-specific contextualized word representations. Our method has reached the accuracy of 76.24% and spearman of 17.12% on the BioNLP 2019 QA task.


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Learning Bilingual Sentiment Word Embeddings for Cross-language Sentiment Classification
HuiWei Zhou | Long Chen | Fulin Shi | Degen Huang
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)


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Combining Syntactic and Semantic Features by SVM for Unrestricted Coreference Resolution
Huiwei Zhou | Yao Li | Degen Huang | Yan Zhang | Chunlong Wu | Yuansheng Yang
Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task


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Exploiting Multi-Features to Detect Hedges and their Scope in Biomedical Texts
Huiwei Zhou | Xiaoyan Li | Degen Huang | Zezhong Li | Yuansheng Yang
Proceedings of the Fourteenth Conference on Computational Natural Language Learning – Shared Task