Summary of Meta-rtl: Reinforcement-based Meta-transfer Learning For Low-resource Commonsense Reasoning, by Yu Fu et al.
Meta-RTL: Reinforcement-Based Meta-Transfer Learning for Low-Resource Commonsense Reasoning
by Yu Fu, Jie He, Yifan Yang, Qun Liu, Deyi Xiong
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel reinforcement-based framework, Meta-RTL, for meta-transfer learning in low-resource commonsense reasoning tasks. Building upon BERT or ALBERT models, the approach dynamically estimates source task weights based on their relevance to the target task, improving performance on three benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The idea is simple: instead of treating all source tasks equally, Meta-RTL learns to assign weights to each source task based on how well it relates to the target task. This is achieved through a reinforcement learning module that uses rewards from the difference between general and task-specific losses. The policy network, built upon LSTMs, captures long-term dependencies in source task weight estimation across meta learning iterations. |
Keywords
» Artificial intelligence » Bert » Meta learning » Reinforcement learning » Transfer learning
