Papers with assignment process

1 papers
Hierarchical Text Classification with Reinforced Label Assignment (D19-1)

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Challenge: Existing hierarchical text classification methods make local decisions regarding labels or ignore hierarchy information during inference.
Approach: They propose to learn a Label Assignment Policy via deep reinforcement learning to determine where to place an object and when to stop the assignment process.
Outcome: The proposed method outperforms state-of-the-art methods on five datasets and four base models and achieves an average improvement of 33.4% over flat classifiers.

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