no code implementations • 27 Apr 2024 • Tao Meng, FuChen Zhang, Yuntao Shou, Wei Ai, Nan Yin, Keqin Li
Since consistency and complementarity information correspond to low-frequency and high-frequency information, respectively, this paper revisits the problem of multimodal emotion recognition in conversation from the perspective of the graph spectrum.
no code implementations • 3 Apr 2024 • Paiheng Xu, Jing Liu, Nathan Jones, Julie Cohen, Wei Ai
Assessing instruction quality is a fundamental component of any improvement efforts in the education system.
no code implementations • 14 Mar 2024 • Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan, Yifan Yang, YuHang Zhou, Fuxiao Liu, Tianrui Guan, Haoliang Wang, Tong Yu, Julian McAuley, Wei Ai, Furong Huang
Causal inference has shown potential in enhancing the predictive accuracy, fairness, robustness, and explainability of Natural Language Processing (NLP) models by capturing causal relationships among variables.
no code implementations • 22 Feb 2024 • YuHang Zhou, Xuan Lu, Wei Ai
In the rapidly evolving landscape of social media, the introduction of new emojis in Unicode release versions presents a structured opportunity to explore digital language evolution.
no code implementations • 22 Jan 2024 • YuHang Zhou, Paiheng Xu, Xiyao Wang, Xuan Lu, Ge Gao, Wei Ai
Our objective is to validate the hypothesis that ChatGPT can serve as a viable alternative to human annotators in emoji research and that its ability to explain emoji meanings can enhance clarity and transparency in online communications.
no code implementations • 19 Jan 2024 • JiaYi Du, Yinghao Wu, Wei Ai, Tao Meng, CanHao Xie, Keqin Li
Community Search (CS) aims to identify densely interconnected subgraphs corresponding to query vertices within a graph.
no code implementations • 19 Jan 2024 • Wei Ai, CanHao Xie, Tao Meng, Yinghao Wu, Keqin Li
Community search is a derivative of community detection that enables online and personalized discovery of communities and has found extensive applications in massive real-world networks.
no code implementations • 3 Jan 2024 • Wei Ai, FuChen Zhang, Tao Meng, Yuntao Shou, HongEn Shao, Keqin Li
To address the above issues, we propose a two-stage emotion recognition model based on graph contrastive learning (TS-GCL).
no code implementations • 28 Dec 2023 • Yuntao Shou, Tao Meng, Wei Ai, Keqin Li
However, the existing feature fusion methods have usually mapped the features of different modalities into the same feature space for information fusion, which can not eliminate the heterogeneity between different modalities.
no code implementations • 17 Dec 2023 • Wei Ai, Yuntao Shou, Tao Meng, Keqin Li
Specifically, we construct a weighted multi-relationship graph to simultaneously capture the dependencies between speakers and event relations in a dialogue.
no code implementations • 11 Dec 2023 • Tao Meng, Yuntao Shou, Wei Ai, Nan Yin, Keqin Li
The main task of Multimodal Emotion Recognition in Conversations (MERC) is to identify the emotions in modalities, e. g., text, audio, image and video, which is a significant development direction for realizing machine intelligence.
no code implementations • 10 Dec 2023 • Yuntao Shou, Tao Meng, Wei Ai, Nan Yin, Keqin Li
Unlike the traditional single-utterance multi-modal emotion recognition or single-modal conversation emotion recognition, MCER is a more challenging problem that needs to deal with more complex emotional interaction relationships.
no code implementations • 5 Dec 2023 • Yuntao Shou, Wei Ai, Tao Meng
Furthermore, this paper innovatively introduces information bottleneck theory into graph contrastive learning to maximize task-related information while minimizing task-independent redundant information.
no code implementations • 4 Dec 2023 • Yuntao Shou, Wei Ai, Tao Meng, Keqin Li
Zero-shot age estimation aims to learn feature information about age from input images and make inferences about a given person's image or video frame without specific sample data.
no code implementations • 15 Nov 2023 • YuHang Zhou, Paiheng Xu, Xiaoyu Liu, Bang An, Wei Ai, Furong Huang
We find that LMs, when encountering spurious correlations between a concept and a label in training or prompts, resort to shortcuts for predictions.
no code implementations • 12 Sep 2023 • Ahmed Adel Attia, Jing Liu, Wei Ai, Dorottya Demszky, Carol Espy-Wilson
Recent advancements in Automatic Speech Recognition (ASR) systems, exemplified by Whisper, have demonstrated the potential of these systems to approach human-level performance given sufficient data.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 30 Aug 2023 • YuHang Zhou, Xuan Lu, Ge Gao, Qiaozhu Mei, Wei Ai
In this paper, we study how emoji usage influences developer participation and issue resolution in virtual workspaces.
no code implementations • 1 Jun 2023 • Jing Zhu, YuHang Zhou, Vassilis N. Ioannidis, Shengyi Qian, Wei Ai, Xiang Song, Danai Koutra
While Graph Neural Networks (GNNs) are remarkably successful in a variety of high-impact applications, we demonstrate that, in link prediction, the common practices of including the edges being predicted in the graph at training and/or test have outsized impact on the performance of low-degree nodes.
no code implementations • 25 May 2023 • Paiheng Xu, YuHang Zhou, Bang An, Wei Ai, Furong Huang
Given the growing concerns about fairness in machine learning and the impressive performance of Graph Neural Networks (GNNs) on graph data learning, algorithmic fairness in GNNs has attracted significant attention.
no code implementations • 29 Jan 2023 • Xuan Lu, Wei Ai, Yixin Wang, Qiaozhu Mei
While many organizations have shifted to working remotely during the COVID-19 pandemic, how the remote workforce and the remote teams are influenced by and would respond to this and future shocks remain largely unknown.
no code implementations • 10 Feb 2021 • Xuan Lu, Wei Ai, Zhenpeng Chen, Yanbin Cao, Qiaozhu Mei
This paper studies how emojis, as non-verbal cues in online communications, can be used for such purposes and how the emotional signals in emoji usage can be used to predict future behavior of workers.
no code implementations • 7 Aug 2020 • Teng Ye, Wei Ai, Lingyu Zhang, Ning Luo, Lulu Zhang, Jieping Ye, Qiaozhu Mei
Through interpreting the best-performing models, we discover many novel and actionable insights regarding how to optimize the design and the execution of team competitions on ride-sharing platforms.