论文标题
按顺序完成样本的课程学习自然语言生成
In-sample Curriculum Learning by Sequence Completion for Natural Language Generation
论文作者
论文摘要
课程学习通过培训机器学习模型从简单的样本到硬式培训，表明了多个领域的有希望的改进。以前的工作设计规则或火车模型以高度依赖于特定于任务的专业知识，并且不能概括。受到“易于坚强”的直觉的启发，我们建议为自然语言生成任务进行样本课程学习。我们的学习策略开始训练模型以生成最后几个单词，即完成序列完成，并逐渐扩展以生成整个输出序列。综合实验表明，它可以很好地概括到不同的任务，并比强基础实现了重大改进。
Curriculum learning has shown promising improvements in multiple domains by training machine learning models from easy samples to hard ones. Previous works which either design rules or train models for scoring the difficulty highly rely on task-specific expertise, and cannot generalize. Inspired by the "easy-to-hard" intuition, we propose to do in-sample curriculum learning for natural language generation tasks. Our learning strategy starts training the model to generate the last few words, i.e., do sequence completion, and gradually extends to generate the whole output sequence. Comprehensive experiments show that it generalizes well to different tasks and achieves significant improvements over strong baselines.
