论文标题
有效使用T5的可解释自然语言推断的有效的跨任务转移学习
Effective Cross-Task Transfer Learning for Explainable Natural Language Inference with T5
论文作者
论文摘要
我们将顺序微调与用于多任务学习的模型进行比较，在我们有兴趣提高两个任务的性能的上下文中，其中一项取决于另一个任务。我们在Figlang2022共享任务上测试了这些模型，该任务要求参与者预测图形语言上的语言推论标签，以及对推理预测的相应文本解释。我们的结果表明，虽然可以将连续的多任务学习调整为在两个目标任务中的第一个方面都是良好的，但在第二个目标任务中，它的表现不佳，并且在过度拟合方面的努力又努力。我们的发现表明，文本到文本模型的简单顺序微调是一种非常强大的方法，用于交叉任务知识转移，同时预测多个相互依存的目标。如此之多，以至于我们最好的模型在任务上达到了最高分数。
We compare sequential fine-tuning with a model for multi-task learning in the context where we are interested in boosting performance on two tasks, one of which depends on the other. We test these models on the FigLang2022 shared task which requires participants to predict language inference labels on figurative language along with corresponding textual explanations of the inference predictions. Our results show that while sequential multi-task learning can be tuned to be good at the first of two target tasks, it performs less well on the second and additionally struggles with overfitting. Our findings show that simple sequential fine-tuning of text-to-text models is an extraordinarily powerful method for cross-task knowledge transfer while simultaneously predicting multiple interdependent targets. So much so, that our best model achieved the (tied) highest score on the task.
