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- W2783136244 abstract "Inspired by the notion of a curriculum that allows human learners to acquire knowledge from easy to difficult materials, curriculum learning (CL) has been applied to many areas including Natural Language Processing (NLP). Most previous CL methods in NLP learn texts according to their lengths. We posit, however, that learning semantically similar texts is more effective than simply relying on superficial easiness such as text lengths. As such, we propose a new CL method that considers semantic dissimilarity as the complexity measure and a tree-structured curriculum as the organization method. The proposed CL method shows better performance than previous CL methods on a sentiment analysis task in an experiment." @default.
- W2783136244 created "2018-01-26" @default.
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- W2783136244 date "2017-12-01" @default.
- W2783136244 modified "2023-09-23" @default.
- W2783136244 title "Tree-Structured Curriculum Learning Based on Semantic Similarity of Text" @default.
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- W2783136244 doi "https://doi.org/10.1109/icmla.2017.00-27" @default.
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