Qingnan Jiang, Lei Chen, Ruifeng Xu, Xiang Ao, Min Yang. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Yi Chen, Rui Wang, Haiyun Jiang, Shuming Shi, Ruifeng Xu. Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023 (Findings). 2023.

Relation extraction (RE) involves identifying the relations between entities from underlying content. RE serves as the foundation for many natural language processing (NLP) and information retrieval applications, such as knowledge graph completion and question answering. In recent years, deep neural networks have dominated the field of RE and made noticeable progress. Subsequently, the large pre-trained language models (PLMs) have taken the state-of-the-art RE to a new level. This survey provides a comprehensive review of existing deep learning techniques for RE. First, we introduce RE resources, including datasets and evaluation metrics. Second, we propose a new taxonomy to categorize existing works from three perspectives, i.e., text representation, context encoding, and triplet prediction. Third, we discuss several important challenges faced by RE and summarize potential techniques to tackle these challenges. Finally, we outline some promising future directions and prospects in this field. This survey is expected to facilitate researchers’ collaborative efforts to address the challenges of real-world RE systems.

Feiteng Fang, Yuelin Bai, Shiwen Ni, Min Yang, Xiaojun Chen, Ruifeng Xu. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Identifying sarcastic clues from both textual and visual information has become an important research issue, called Multimodal Sarcasm Detection. In this paper, we investigate multimodal sarcasm detection from a novel perspective, where a multimodal graph contrastive learning strategy is proposed to fuse and distinguish the sarcastic clues for textual modality and visual modality. Specifically, we first utilize object detection to derive the crucial visual regions accompanied by their captions of the images, which allows better learning of the key visual regions of visual modality. In addition, to make full use of the semantic information of the visual modality, we employ optical character recognition to extract the textual content in the images. Then, based on image regions, the textual content of visual modality, and the context of the textual modality, we build a multimodal graph for each sample to model the intricate sarcastic relations between modalities. Furthermore, we devise a graph-oriented contrastive learning strategy to leverage the correlations in the same label and differences between different labels, so as to capture better multimodal representations for multimodal sarcasm detection. Extensive experiments show that our method outperforms the previous best baseline models (with a 2.47% improvement in Accuracy, a 1.99% improvement in F-score, and a 2.20% improvement in Macro F-score). The ablation study shows that both multimodal graph structure and graph-oriented contrastive learning are important to our framework. Further, the experiments of using different pre-trained methods show that the proposed multimodal graph contrastive learning framework can directly work with various pre-trained models and achieve outstanding performance in multimodal sarcasm detection.

Shijue Huang, Wanjun Zhong, Jianqiao Lu, Qi Zhu, Jiahui Gao, Weiwen Liu, Yutai Hou, Xingshan Zeng, Yasheng Wang, Lifeng Shang, Xin Jiang, Ruifeng Xu, Qun Liu. Findings of the Association for Computational Linguistics ACL 2024. 2024.