Zengfeng Huang


基于多质心异质图学习的社交网络用户建模(User Representation Learning based on Multi-centroid Heterogeneous Graph Neural Networks)
Shangyi Ning (宁上毅) | Guanying Li (李冠颖) | Qin Chen (陈琴) | Zengfeng Huang (黄增峰) | Baohua Zhou (周葆华) | Zhongyu Wei (魏忠钰)
Proceedings of the 20th Chinese National Conference on Computational Linguistics



Automatic Term Name Generation for Gene Ontology: Task and Dataset
Yanjian Zhang | Qin Chen | Yiteng Zhang | Zhongyu Wei | Yixu Gao | Jiajie Peng | Zengfeng Huang | Weijian Sun | Xuanjing Huang
Findings of the Association for Computational Linguistics: EMNLP 2020

Terms contained in Gene Ontology (GO) have been widely used in biology and bio-medicine. Most previous research focuses on inferring new GO terms, while the term names that reflect the gene function are still named by the experts. To fill this gap, we propose a novel task, namely term name generation for GO, and build a large-scale benchmark dataset. Furthermore, we present a graph-based generative model that incorporates the relations between genes, words and terms for term name generation, which exhibits great advantages over the strong baselines.

PathQG: Neural Question Generation from Facts
Siyuan Wang | Zhongyu Wei | Zhihao Fan | Zengfeng Huang | Weijian Sun | Qi Zhang | Xuanjing Huang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Existing research for question generation encodes the input text as a sequence of tokens without explicitly modeling fact information. These models tend to generate irrelevant and uninformative questions. In this paper, we explore to incorporate facts in the text for question generation in a comprehensive way. We present a novel task of question generation given a query path in the knowledge graph constructed from the input text. We divide the task into two steps, namely, query representation learning and query-based question generation. We formulate query representation learning as a sequence labeling problem for identifying the involved facts to form a query and employ an RNN-based generator for question generation. We first train the two modules jointly in an end-to-end fashion, and further enforce the interaction between these two modules in a variational framework. We construct the experimental datasets on top of SQuAD and results show that our model outperforms other state-of-the-art approaches, and the performance margin is larger when target questions are complex. Human evaluation also proves that our model is able to generate relevant and informative questions.