WebGoogle Scholar Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, YongDong Zhang, and Meng Wang. 2024. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event, China) (SIGIR '20). WebJul 7, 2024 · Ye Liu, Jianguo Zhang, Yao Wan, Congying Xia, Lifang He, and Philip Yu. 2024. HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.
Congying Xia DeepAI
WebSep 21, 2024 · [Submitted on 21 Sep 2024] Composed Variational Natural Language Generation for Few-shot Intents Congying Xia, Caiming Xiong, Philip Yu, Richard Socher In this paper, we focus on generating training examples for few-shot intents in the realistic imbalanced scenario. WebMehrnaz Najafi (Ph.D., 2024, Machine Learning and Relevance Engineer @ LinkedIn) Nooshin Mojab (Ph.D., 2024, Software Engineer @ Google) Shaika Chowdhury (Ph.D., … fire on the water great yarmouth reviews
Pseudo Siamese Network for Few-shot Intent Generation
WebNov 2, 2024 · Review rating prediction of text reviews is a rapidly growing technology with a wide range of applications in natural language processing. However, most existing methods either use hand-crafted features or learn features using deep learning with simple text corpus as input for review rating prediction, ignoring the hierarchies among data. In this … WebGoogle Scholar; Jiawei Zhang, Haopeng Zhang, Congying Xia, and Li Sun. 2024. Graph-bert: Only attention is needed for learning graph representations. arXiv preprint arXiv:2001.05140 (2024). Google Scholar; Xin Zhang, An Yang, Sujian Li, and Yizhong Wang. 2024. Machine reading comprehension: a literature review. arXiv preprint … WebAug 2, 2024 · This paper fills the gap by reviewing the state-of-the-art approaches from 1961 to 2024, focusing on models from traditional models to deep learning. We create a … fire on the water festival