Abstract
Label scarcity is a bottleneck for improving task performance in specialised domains. We propose a novel compositional transfer learning framework (DoT5 - domain compositional zero-shot T5) for zero-shot domain transfer. Without access to in-domain labels, DoT5 jointly learns domain knowledge (from MLM of unlabelled in-domain free text) and task knowledge (from task training on more readily available general-domain data) in a multi-task manner. To improve the transferability of task training, we design a strategy named NLGU: we simultaneously train NLG for in-domain label-to-data generation which enables data augmentation for self-finetuning and NLU for label prediction. We evaluate DoT5 on the biomedical domain and the resource-lean subdomain of radiology, focusing on NLI, text summarisation and embedding learning. DoT5 demonstrates the effectiveness of compositional transfer learning through multi-task learning. In particular, DoT5 outperforms the current SOTA in zero-shot transfer by over 7 absolute points in accuracy on RadNLI. We validate DoT5 with ablations and a case study demonstrating its ability to solve challenging NLI examples requiring in-domain expertise.
Abstract (translated)
标签稀缺是改善特定领域的任务表现的瓶颈。我们提出了一种全新的组件化 Transfer Learning 框架(DoT5 - 域组件式零次输入 T5),用于零次输入域转移。在没有访问域内标签的情况下,DoT5 以多任务方式共同学习域知识和任务知识(从未标记的域内自由文本的 LM 中提取任务知识,并从任务训练更常见的通用数据集中提取数据增强和 NLU)。为了提高任务训练的可转移性,我们设计了一种名为 NLGU 的策略:我们同时训练 In-domain 标签到数据生成 NLG,这可以实现数据增强的自训练和标签预测的 NLU。我们在生物医学领域和放射学资源受限的子领域评估了 DoT5,重点关注 NLI、文本摘要和嵌入学习。DoT5 通过多任务学习证明了组件化转移学习的 effectiveness。特别是,DoT5 在 RadNLI 上的零次输入转移中比当前的最佳方法高出超过 7 的绝对点的准确性。我们通过实验和案例研究验证了 DoT5 的能力,以解决需要域内专业知识的具有挑战性的 NLI 示例。
URL
https://arxiv.org/abs/2303.13386