MetaXCR：基于强化学习的元迁移方法用于跨语言常识推理
MetaXCR: Reinforcement-Based Meta-Transfer Learning for Cross-Lingual Commonsense Reasoning
AI总结：
本文提出MetaXCR框架，通过多源元学习、强化采样策略和跨语言元适应方法，在低资源条件下利用英语常识推理数据集提升跨语言目标任务的性能。
摘要：
常识推理（CR）已在多个领域得到研究，并借助大规模数据集取得了巨大进展。遗憾的是，现有的大多数常识推理数据集都是用英语构建的，因此以往的工作大多集中在英语上。此外，由于常识推理的标注成本高昂，不可能为每一个新任务都构建大规模数据集。因此，跨语言低资源常识推理的需求日益增长，其目标是利用多种已有的英语数据集，帮助模型在标注数据有限的情况下适应新的跨语言目标数据集。在本文中，我们提出了一种用于跨语言低资源常识推理的多源适配器（MetaXCR）。在该框架中，我们首先通过引入多个训练数据集来扩展元学习，以学习跨不同任务的通用任务适配器。然后，我们进一步引入一种基于强化学习的采样策略，帮助模型采样对目标任务最有帮助的源任务。最后，我们引入两种跨语言元适应方法来提升模型在目标语言上的性能。大量实验表明，MetaXCR优于当前最先进的方法，同时训练所用的参数量少于其他工作。
英文摘要：
Commonsense reasoning (CR) has been studied in many pieces of domain and has achieved great progress with the aid of large datasets. Unfortunately, most existing CR datasets are built in English, so most previous work focus on English. Furthermore, as the annotation of commonsense reasoning is costly, it is impossible to build a large dataset for every novel task. Therefore, there are growing appeals for Cross-lingual Low-Resource Commonsense Reasoning, which aims to leverage diverse existed English datasets to help the model adapt to new cross-lingual target datasets with limited labeled data. In this paper, we propose a multi-source adapter for cross-lingual low-resource Commonsense Reasoning (MetaXCR). In this framework, we first extend meta learning by incorporating multiple training datasets to learn a generalized task adapters across different tasks. Then, we further introduce a reinforcement-based sampling strategy to help the model sample the source task that is the most helpful to the target task. Finally, we introduce two types of cross-lingual meta-adaption methods to enhance the performance of models on target languages. Extensive experiments demonstrate MetaXCR is superior over state-of-the-arts, while being trained with fewer parameters than other work.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
