Multi-Task Label Embedding for Text Classification
By Honglun Zhang, Liqiang Xiao, Wenqing Chen, Yongkun Wang, Yaohui Jin. 2017
101 citations recorded by the source corpus.
Multi-task learning in text classification leverages implicit correlations among related tasks to extract common features and yield performance gains. However, a large body of previous work treats labels of each task as independent and meaningless one-hot vectors, which cause a loss of potential label information. In this paper, we propose Multi-Task Label Embedding to convert labels in text classification into semantic vectors, thereby turning the original tasks into vector matching tasks. Our model utilizes semantic correlations among tasks and makes it convenient to scale or transfer when new tasks are involved. Extensive experiments on five benchmark datasets for text classification show that our model can effectively improve the performances of related tasks with semantic representations of labels and additional information from each other.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Chunyuan Li
- Lawrence Carin
- Dinghan Shen
- Ricardo Henao
- Yizhe Zhang
- Guoyin Wang
- Kai-Wei Chang
- Jianfeng Gao
Citations in the corpus
- Joint Embedding of Words and Labels for Text Classification (2018, methodology): “, 2015) and multitask learning in (Zhang et al., 2017a), respectively.”
- Multi-task Learning for Universal Sentence Embeddings: A Thorough Evaluation using Transfer and Auxiliary Tasks (2018, background): “Liu et al. (2016b); Zhang et al. (2017b) proposed multi-task learning architecture with different methods of sharing information, and can be further enhanced with an external memory (Liu et al., 2016a) which is shared across the participant tasks.”