论文标题
通过通过增强学习来微调变压器，有效的无监督句子压缩
Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement Learning
论文作者
论文摘要
句子压缩通过删除非必需内容的同时保留重要事实和语法性来减少文本长度。无需监督的句子压缩的客观驱动方法可用于创建自定义模型，而无需进行基础真相训练数据，同时允许在学习和推理的目标函数中灵活性。最近的无监督句子压缩方法使用自定义目标来指导离散搜索；但是，指导搜索在推理时很昂贵。在这项工作中，我们探讨了使用强化学习来训练有效的句子压缩模型，这些模型在产生预测时也很快。特别是，我们使用简单的策略梯度方法将任务作为二进制序列标记并微调预训练的变压器。我们的方法表现优于其他无监督的模型，同时在推理时间更有效。
Sentence compression reduces the length of text by removing non-essential content while preserving important facts and grammaticality. Unsupervised objective driven methods for sentence compression can be used to create customized models without the need for ground-truth training data, while allowing flexibility in the objective function(s) that are used for learning and inference. Recent unsupervised sentence compression approaches use custom objectives to guide discrete search; however, guided search is expensive at inference time. In this work, we explore the use of reinforcement learning to train effective sentence compression models that are also fast when generating predictions. In particular, we cast the task as binary sequence labelling and fine-tune a pre-trained transformer using a simple policy gradient approach. Our approach outperforms other unsupervised models while also being more efficient at inference time.
