The three Multi-Label datasets used in the article "Adapting Transformers for Multi-Label Text Classification". - AAPD Dataset (ArXiv Academic Paper Dataset) [Yang et al. 2018]1 - Reuters-21578 Dataset: https://archive.ics.uci.edu/ml/datasets/reuters-21578+text+categorization+collection - MFHAD (Multilabel French HAL Abstracts Dataset) 1Pengcheng Yang, Xu Sun, Wei Li, Shuming Ma, Wei Wu, and Houfeng Wang. 2018. SGM: Sequence Generation Model for Multi-label Classification. In Proceedings of the 27th International Conference on Computational Linguistics. Association for Computational Linguistics, Santa Fe, New Mexico, USA, 3915–3926
Multi-label classification is relevant to many domains, such as text, image and other media, and bio...
Document classification is a large body of search, many approaches were proposed for single label an...
Document classification is a large body of search, many approaches were proposed for single label an...
The three Multi-Label datasets used in the article "Adapting Transformers for Multi-Label Text Class...
International audiencePre-trained language models have proven to be effective in multi-class text cl...
International audienceWe introduce a new approach to improve and adapt transformers for multi-label ...
We introduce in this paper a new approach to improve deep learningbased architectures for multi-labe...
Existing multilabel text classification methods rely on a complex manual design to mine label correl...
24th Irish Conference on Artificial Intelligence and Cognitive Science (AICS'16), Dublin, Ireland, 2...
Multi-label classification is a generalization of a broader concept of multi-class classification in...
Multi-label text classification (MLTC) aims to annotate documents with the most relevant labels from...
Abstract—Multi label classification is concerned with learning from a set of instances that are asso...
Research on multi-label classification is concerned with developing and evaluating algorithms that l...
The classification of scientific articles aligned to Sustainable Development Goals is crucial for re...
24th Irish Conference on Artificial Intelligence and Cognitive Science (AICS'16), Dublin, Ireland, 2...
Multi-label classification is relevant to many domains, such as text, image and other media, and bio...
Document classification is a large body of search, many approaches were proposed for single label an...
Document classification is a large body of search, many approaches were proposed for single label an...
The three Multi-Label datasets used in the article "Adapting Transformers for Multi-Label Text Class...
International audiencePre-trained language models have proven to be effective in multi-class text cl...
International audienceWe introduce a new approach to improve and adapt transformers for multi-label ...
We introduce in this paper a new approach to improve deep learningbased architectures for multi-labe...
Existing multilabel text classification methods rely on a complex manual design to mine label correl...
24th Irish Conference on Artificial Intelligence and Cognitive Science (AICS'16), Dublin, Ireland, 2...
Multi-label classification is a generalization of a broader concept of multi-class classification in...
Multi-label text classification (MLTC) aims to annotate documents with the most relevant labels from...
Abstract—Multi label classification is concerned with learning from a set of instances that are asso...
Research on multi-label classification is concerned with developing and evaluating algorithms that l...
The classification of scientific articles aligned to Sustainable Development Goals is crucial for re...
24th Irish Conference on Artificial Intelligence and Cognitive Science (AICS'16), Dublin, Ireland, 2...
Multi-label classification is relevant to many domains, such as text, image and other media, and bio...
Document classification is a large body of search, many approaches were proposed for single label an...
Document classification is a large body of search, many approaches were proposed for single label an...