论文标题
适配器：语言模型的客观以中心的适应框架
Adaptor: Objective-Centric Adaptation Framework for Language Models
论文作者
论文摘要
自然语言处理研究的进展是由广泛的软件框架所带来的可能性催化的。本文介绍了适配器库，该库转换传统的以模型为中心的方法，该方法由培训前 +微调步骤组成，以客观为中心的方法，并通过应用所选目标的应用来构成培训过程。我们调查了可以从多任务培训，自定义目标发展，动态培训课程或领域适应的增强目标实验中受益的研究方向。适配器旨在在实践中简化这些研究方向的可重复性。最后，我们证明了适配器在选定的无监督域适应方案中的实际适用性。
Progress in natural language processing research is catalyzed by the possibilities given by the widespread software frameworks. This paper introduces Adaptor library that transposes the traditional model-centric approach composed of pre-training + fine-tuning steps to objective-centric approach, composing the training process by applications of selected objectives. We survey research directions that can benefit from enhanced objective-centric experimentation in multitask training, custom objectives development, dynamic training curricula, or domain adaptation. Adaptor aims to ease reproducibility of these research directions in practice. Finally, we demonstrate the practical applicability of Adaptor in selected unsupervised domain adaptation scenarios.
