论文标题
探索数据内任务转移中的数据效率，以了解对话
An Exploration of Data Efficiency in Intra-Dataset Task Transfer for Dialog Understanding
论文作者
论文摘要
转移学习是自然语言处理的一个令人兴奋的领域，具有提高模型性能并提高数据效率的潜力。这项研究探讨了不同数量的目标任务训练数据对对话域中连续转移学习的影响。我们假设模型可以利用从源任务中汲取的信息来更好地学习目标任务，从而减少所需的目标任务培训样本的数量。我们的数据毫不局限地表明，与没有传输学习的同一模型相比，对任务训练数据大小通常对顺序转移学习的性能最小。我们的结果使我们相信，这种意外的结果可能是由于灾难性遗忘的影响，激发了进一步的工作，以防止这种遗忘。
Transfer learning is an exciting area of Natural Language Processing that has the potential to both improve model performance and increase data efficiency. This study explores the effects of varying quantities of target task training data on sequential transfer learning in the dialog domain. We hypothesize that a model can utilize the information learned from a source task to better learn a target task, thereby reducing the number of target task training samples required. Unintuitively, our data shows that often target task training data size has minimal effect on how sequential transfer learning performs compared to the same model without transfer learning. Our results lead us to believe that this unexpected result could be due to the effects of catastrophic forgetting, motivating further work into methods that prevent such forgetting.
