Meta-RTL: Reinforcement-Based Meta-Transfer Learning for Low-Resource Commonsense Reasoning

• URL: http://arxiv.org/abs/2409.19075v1
• Date: Fri, 27 Sep 2024 18:22:22 GMT
• Title: Meta-RTL: Reinforcement-Based Meta-Transfer Learning for Low-Resource Commonsense Reasoning
• Authors: Yu Fu, Jie He, Yifan Yang, Qun Liu, Deyi Xiong,
• Abstract summary: We propose a reinforcement-based multi-source meta-transfer learning framework (Meta-RTL) for low-resource commonsense reasoning. We present a reinforcement-based approach to dynamically estimating source task weights that measure the contribution of the corresponding tasks to the target task in the meta-transfer learning. Experimental results demonstrate that Meta-RTL substantially outperforms strong baselines and previous task selection strategies.
• Score: 61.8360232713375
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Meta learning has been widely used to exploit rich-resource source tasks to improve the performance of low-resource target tasks. Unfortunately, most existing meta learning approaches treat different source tasks equally, ignoring the relatedness of source tasks to the target task in knowledge transfer. To mitigate this issue, we propose a reinforcement-based multi-source meta-transfer learning framework (Meta-RTL) for low-resource commonsense reasoning. In this framework, we present a reinforcement-based approach to dynamically estimating source task weights that measure the contribution of the corresponding tasks to the target task in the meta-transfer learning. The differences between the general loss of the meta model and task-specific losses of source-specific temporal meta models on sampled target data are fed into the policy network of the reinforcement learning module as rewards. The policy network is built upon LSTMs that capture long-term dependencies on source task weight estimation across meta learning iterations. We evaluate the proposed Meta-RTL using both BERT and ALBERT as the backbone of the meta model on three commonsense reasoning benchmark datasets. Experimental results demonstrate that Meta-RTL substantially outperforms strong baselines and previous task selection strategies and achieves larger improvements on extremely low-resource settings.

Related papers

• Task-Distributionally Robust Data-Free Meta-Learning [99.56612787882334]
  Data-Free Meta-Learning (DFML) aims to efficiently learn new tasks by leveraging multiple pre-trained models without requiring their original training data. For the first time, we reveal two major challenges hindering their practical deployments: Task-Distribution Shift ( TDS) and Task-Distribution Corruption (TDC)
  arXiv  Detail & Related papers  (2023-11-23T15:46:54Z)
• Meta-Reinforcement Learning Based on Self-Supervised Task Representation Learning [23.45043290237396]
  MoSS is a context-based Meta-reinforcement learning algorithm based on Self-Supervised task representation learning. On MuJoCo and Meta-World benchmarks, MoSS outperforms prior in terms of performance, sample efficiency (3-50x faster), adaptation efficiency, and generalization.
  arXiv  Detail & Related papers  (2023-04-29T15:46:19Z)
• Learning Action Translator for Meta Reinforcement Learning on Sparse-Reward Tasks [56.63855534940827]
  This work introduces a novel objective function to learn an action translator among training tasks. We theoretically verify that the value of the transferred policy with the action translator can be close to the value of the source policy. We propose to combine the action translator with context-based meta-RL algorithms for better data collection and more efficient exploration during meta-training.
  arXiv  Detail & Related papers  (2022-07-19T04:58:06Z)
• Robust Meta-Reinforcement Learning with Curriculum-Based Task Sampling [0.0]
  We show that Robust Meta Reinforcement Learning with Guided Task Sampling (RMRL-GTS) is an effective method that restricts task sampling based on scores and epochs. In order to achieve robust meta-RL, it is necessary not only to intensively sample tasks with poor scores, but also to restrict and expand the task regions of the tasks to be sampled.
  arXiv  Detail & Related papers  (2022-03-31T05:16:24Z)
• Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation [87.98063273826702]
  We propose a memory imitation meta-learning (MemIML) method that enhances the model's reliance on support sets for task adaptation. A theoretical analysis is provided to prove the effectiveness of our method.
  arXiv  Detail & Related papers  (2022-03-22T12:41:55Z)
• Transfer Meta-Learning: Information-Theoretic Bounds and Information Meta-Risk Minimization [47.7605527786164]
  Meta-learning automatically infers an inductive bias by observing data from a number of related tasks. We introduce the problem of transfer meta-learning, in which tasks are drawn from a target task environment during meta-testing.
  arXiv  Detail & Related papers  (2020-11-04T12:55:43Z)
• MetaCURE: Meta Reinforcement Learning with Empowerment-Driven Exploration [52.48362697163477]
  Experimental evaluation shows that our meta-RL method significantly outperforms state-of-the-art baselines on sparse-reward tasks. We model an exploration policy learning problem for meta-RL, which is separated from exploitation policy learning. We develop a new off-policy meta-RL framework, which efficiently learns separate context-aware exploration and exploitation policies.
  arXiv  Detail & Related papers  (2020-06-15T06:56:18Z)
• Curriculum in Gradient-Based Meta-Reinforcement Learning [10.447238563837173]
  We show that gradient-based meta-learners are sensitive to task distributions. With the wrong curriculum, agents suffer the effects of meta-overfitting, shallow adaptation, and adaptation instability.
  arXiv  Detail & Related papers  (2020-02-19T01:40:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.