1 code implementation • 12 Jan 2024 • Wenyuan Zhang, Xinghua Zhang, Shiyao Cui, Kun Huang, Xuebin Wang, Tingwen Liu
Aspect sentiment quad prediction (ASQP) aims to predict the quad sentiment elements for a given sentence, which is a critical task in the field of aspect-based sentiment analysis.
1 code implementation • 30 Nov 2023 • Shiyao Cui, Zhenyu Zhang, Yilong Chen, Wenyuan Zhang, Tianyun Liu, Siqi Wang, Tingwen Liu
The widespread of generative artificial intelligence has heightened concerns about the potential harms posed by AI-generated texts, primarily stemming from factoid, unfair, and toxic content.
no code implementations • 4 Aug 2023 • Shiyao Cui, Xin Cong, Jiawei Sheng, Xuebin Wang, Tingwen Liu, Jinqiao Shi
In this paper, we regard public pre-trained language models as knowledge bases and automatically mine the script-related knowledge via prompt-learning.
no code implementations • 19 Jun 2023 • Qian Li, Shu Guo, Cheng Ji, Xutan Peng, Shiyao Cui, JianXin Li
Multi-Modal Relation Extraction (MMRE) aims at identifying the relation between two entities in texts that contain visual clues.
no code implementations • 5 Apr 2023 • Shiyao Cui, Jiangxia Cao, Xin Cong, Jiawei Sheng, Quangang Li, Tingwen Liu, Jinqiao Shi
For the first issue, a refinement-regularizer probes the information-bottleneck principle to balance the predictive evidence and noisy information, yielding expressive representations for prediction.
no code implementations • 19 Mar 2023 • Hongmeng Liu, Jiapeng Zhao, Yixuan Huo, Yuyan Wang, Chun Liao, Liyan Shen, Shiyao Cui, Jinqiao Shi
Traditional user representation methods mainly rely on modeling the text information of posts and cannot capture the temporal content and the forum interaction of posts.
1 code implementation • COLING 2022 • Shiyao Cui, Jiawei Sheng, Xin Cong, Quangang Li, Tingwen Liu, Jinqiao Shi
Event Causality Identification (ECI), which aims to detect whether a causality relation exists between two given textual events, is an important task for event causality understanding.
1 code implementation • SIGIR 2022 • Xin Cong, Jiawei Sheng, Shiyao Cui, Bowen Yu, Tingwen Liu, Bin Wang
To instantiate this strategy, we further propose a model, RelATE, which builds a dual-level attention to aggregate relationrelevant information to detect the relation occurrence and utilizes the annotated samples of the detected relations to extract the corresponding head/tail entities.
no code implementations • 7 Feb 2022 • Shiyao Cui, Xin Cong, Bowen Yu, Tingwen Liu, Yucheng Wang, Jinqiao Shi
Meanwhile, rough reading is explored in a multi-round manner to discover undetected events, thus the multi-events problem is handled.
no code implementations • 5 Jul 2021 • Qian Li, JianXin Li, Jiawei Sheng, Shiyao Cui, Jia Wu, Yiming Hei, Hao Peng, Shu Guo, Lihong Wang, Amin Beheshti, Philip S. Yu
Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey.
1 code implementation • Findings (ACL) 2021 • Xin Cong, Shiyao Cui, Bowen Yu, Tingwen Liu, Yubin Wang, Bin Wang
Event detection tends to struggle when it needs to recognize novel event types with a few samples.
no code implementations • 3 Dec 2020 • Shiyao Cui, Bowen Yu, Xin Cong, Tingwen Liu, Quangang Li, Jinqiao Shi
A heterogeneous graph attention networks is then introduced to propagate relational message and enrich information interaction.
1 code implementation • 23 Jun 2020 • Xin Cong, Bowen Yu, Tingwen Liu, Shiyao Cui, Hengzhu Tang, Bin Wang
We first build a representation extractor to derive features for unlabeled data from the target domain (no test data is necessary) and then group them with a cluster miner.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Shiyao Cui, Bowen Yu, Tingwen Liu, Zhen-Yu Zhang, Xuebin Wang, Jinqiao Shi
Previous studies on the task have verified the effectiveness of integrating syntactic dependency into graph convolutional networks.