Landmark topological coverings derived from traversibility metrics enable three transfer mechanisms with theoretical Q-value bounds in goal-based multi-task lifelong RL.
Deep Semi-Supervised Learning with Linguistically Motivated Sequence Labeling Task Hierarchies
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abstract
In this paper we present a novel Neural Network algorithm for conducting semi-supervised learning for sequence labeling tasks arranged in a linguistically motivated hierarchy. This relationship is exploited to regularise the representations of supervised tasks by backpropagating the error of the unsupervised task through the supervised tasks. We introduce a neural network where lower layers are supervised by junior downstream tasks and the final layer task is an auxiliary unsupervised task. The architecture shows improvements of up to two percentage points F1 for Chunking compared to a plausible baseline.
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cs.LG 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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On mechanisms for transfer using landmark value functions in multi-task lifelong reinforcement learning
Landmark topological coverings derived from traversibility metrics enable three transfer mechanisms with theoretical Q-value bounds in goal-based multi-task lifelong RL.