Paper Reading AI Learner

GUDN A novel guide network for extreme multi-label text classification

2022-01-10 07:33:36
Qing Wang, Hongji Shu, Jia Zhu
arXiv_CL
       
Abstract

The problem of extreme multi-label text classification (XMTC) is to recall some most relevant labels for a text from an extremely large label set. Though the methods based on deep pre-trained models have reached significant achievement, the pre-trained models are still not fully utilized. Label semantics has not attracted much attention so far, and the latent space between texts and labels has not been effectively explored. This paper constructs a novel guide network (GUDN) to help fine-tune the pre-trained model to instruct classification later. Also, we use the raw label semantics to effectively explore the latent space between texts and labels, which can further improve predicted accuracy. Experimental results demonstrate that GUDN outperforms state-of-the-art methods on several popular datasets. Our source code is released at this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2201.11582

PDF

https://arxiv.org/pdf/2201.11582.pdf

Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot