Abstract
This paper studies the problem of learning a sequence of sentiment classification tasks. The learned knowledge from each task is retained and used to help future or subsequent task learning. This learning paradigm is called Lifelong Learning (LL). However, existing LL methods either only transfer knowledge forward to help future learning and do not go back to improve the model of a previous task or require the training data of the previous task to retrain its model to exploit backward/reverse knowledge transfer. This paper studies reverse knowledge transfer of LL in the context of naive Bayesian (NB) classification. It aims to improve the model of a previous task by leveraging future knowledge without retraining using its training data. This is done by exploiting a key characteristic of the generative model of NB. That is, it is possible to improve the NB classifier for a task by improving its model parameters directly by using the retained knowledge from other tasks. Experimental results show that the proposed method markedly outperforms existing LL baselines.
Abstract (translated)
本文研究了一系列情绪分类任务的学习问题。从每个任务中学习到的知识将被保留并用于帮助将来或以后的任务学习。这种学习模式称为终身学习（ll）。然而，现有的LL方法要么只是将知识向前转移，以帮助将来的学习，要么不回去改进先前任务的模型，要么要求先前任务的训练数据重新训练其模型，以利用向后/逆向知识转移。本文在朴素贝叶斯（NB）分类的背景下研究了LL的逆向知识转移。它的目标是通过利用未来的知识而不使用其培训数据进行再培训来改进以前任务的模型。这是通过开发NB生成模型的一个关键特性来实现的。也就是说，可以通过使用来自其他任务的保留知识，直接改进任务的模型参数来改进NB分类器。实验结果表明，该方法明显优于现有的LL基线。
URL
https://arxiv.org/abs/1906.03506
