DAMTRNN: A Delta Attention-Based Multi-task RNN for Intention Recognition

Chen, Weitong; Yue, Lin; Li, Bohan; Wang, Can; Sheng, Quan Z.

doi:10.1007/978-3-030-35231-8_27

Cited by 13 publications

(2 citation statements)

References 27 publications

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“…Wang et al [22] imitate the cognitive structure learning process into the HMLTC learning and propose a unified framework. Chen et al [23] recognize human intentions from electroencephalographic (EEG) signals and explore correlations between specific segments of an EEG signal and an associated intention.…”

Section: Related Workmentioning

confidence: 99%

UMP-MG: A Uni-directed Message-Passing Multi-label Generation Model for Hierarchical Text Classification

et al. 2023

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Hierarchical Text Classification (HTC) is a formidable task which involves classifying textual descriptions into a taxonomic hierarchy. Existing methods, however, have difficulty in adequately modeling the hierarchical label structures, because they tend to focus on employing graph embedding methods to encode the hierarchical structure while disregarding the fact that the HTC labels are rooted in a tree structure. This is significant because, unlike a graph, the tree structure inherently has a directive that ordains information flow from one node to another—a critical factor when applying graph embedding to the HTC task. But in the graph structure, message-passing is undirected, which will lead to the imbalance of message transmission between nodes when applied to HTC. To this end, we propose a unidirectional message-passing multi-label generation model for HTC, referred to as UMP-MG. Instead of viewing HTC as a classification problem as previous methods have done, this novel approach conceptualizes it as a sequence generation task, introducing prior hierarchical information during the decoding process. This further enables the blocking of information flow in one direction to ensure that the graph embedding method is better suited for the HTC task and thus resulted in the enhanced tree structure representation. Results obtained through experimentation on both the public WOS dataset and an E-commerce user intent classification dataset demonstrate that our proposed model can achieve superlative results.

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Section: Related Workmentioning

confidence: 99%

UMP-MG: A Uni-directed Message-Passing Multi-label Generation Model for Hierarchical Text Classification

et al. 2023

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“…In the following work, we intend to use the neural metric factorization model for a number of related areas in medical research [17][18][19][20][21][22][23], and we believe that this model may be useful for applications such as disease prediction.…”

Section: Summary and Prospectmentioning

confidence: 99%

Neural Metric Factorization for Recommendation

et al. 2022

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All current recommendation algorithms, when modeling user–item interactions, basically use dot product. This dot product calculation is derived from matrix factorization. We argue that an inherent drawback of matrix factorization is that latent semantic vectors of users or items sometimes do not satisfy triangular inequalities, which may affect the performance of the recommendation. Recently, metric factorization was proposed to replace matrix factorization and has achieved some improvements in terms of recommendation accuracy. However, similar to matrix factorization, metric factorization still uses a simple, linear fashion. In this paper, we explore leveraging nonlinear deep neural networks to realize Euclidean distance interaction between users and items. We propose a generic Neural Metric Factorization Framework (NMetricF), which learns representations for users and items by incorporating Euclidean metric factorization into deep neural networks. Extensive experiments on six real-world datasets show that, compared to the previous recommendation algorithms based purely on rating data, NMetricF achieves the best performance.

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FreeSee: A Parameter-Independent Pattern-Based Device-Free Human Behaviour Sensing System with Wireless Signals of IoT Devices

Sun

Zhang

et al. 2022

Lecture Notes in Computer Science

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DAMTRNN: A Delta Attention-Based Multi-task RNN for Intention Recognition

Cited by 13 publications

References 27 publications

UMP-MG: A Uni-directed Message-Passing Multi-label Generation Model for Hierarchical Text Classification

UMP-MG: A Uni-directed Message-Passing Multi-label Generation Model for Hierarchical Text Classification

Neural Metric Factorization for Recommendation

FreeSee: A Parameter-Independent Pattern-Based Device-Free Human Behaviour Sensing System with Wireless Signals of IoT Devices

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