Heng Ji

A Survey of Knowledge-enhanced Text Generation

ACM Computing Surveys · 2022 · 240 citations

The goal of text-to-text generation is to make machines express like a human in many applications such as conversation, summarization, and translation. It is one of the most important yet challenging tasks in natural language processing (NLP). Various neural encoder-decoder models have been proposed to achieve the goal by learning to map input text to output text. However, the input text alone often provides limited knowledge to generate the desired output, so the performance of text generation…

Text Classification Using Label Names Only: A Language Model Self-Training Approach

2020 · 203 citations

Current text classification methods typically require a good number of human-labeled documents as training data, which can be costly and difficult to obtain in real applications. Humans can perform classification without seeing any labeled examples but only based on a small set of words describing the categories to be classified. In this paper, we explore the potential of only using the label name of each class to train classification models on unlabeled data, without using any labeled documents…

Efficient Attentions for Long Document Summarization

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · 2021 · 132 citations

Luyang Huang, Shuyang Cao, Nikolaus Parulian, Heng Ji, Lu Wang. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation

2021 · 106 citations

Qingyun Wang, Manling Li, Xuan Wang, Nikolaus Parulian, Guangxing Han, Jiawei Ma, Jingxuan Tu, Ying Lin, Ranran Haoran Zhang, Weili Liu, Aabhas Chauhan, Yingjun Guan, Bangzheng Li, Ruisong Li, Xiangchen Song, Yi Fung, Heng Ji, Jiawei Han, Shih-Fu Chang, James Pustejovsky, Jasmine Rah, David Liem, Ahmed ELsayed, Martha Palmer, Clare Voss, Cynthia Schneider, Boyan Onyshkevych. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human L…

CLIP-Event: Connecting Text and Images with Event Structures

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 2022 · 104 citations

Vision-language (V+L) pretraining models have achieved great success in supporting multimedia applications by understanding the alignments between images and text. While existing vision-language pretraining models primarily focus on understanding objects in images or entities in text, they often ignore the alignment at the level of events and their argument structures. In this work, we propose a contrastive learning framework to enforce vision-language pretraining models to comprehend events and…