论文标题
通过非自动性模型的文本编辑的模仿学习课程
An Imitation Learning Curriculum for Text Editing with Non-Autoregressive Models
论文作者
论文摘要
我们提出了一个框架，用于训练非自动向序列到序列模型进行编辑任务，其中原始输入序列经过迭代编辑以产生输出。我们表明，旨在训练此类机器翻译模型的模仿学习算法引入了训练和推理之间的不匹配，从而导致训练不足和编辑场景中的概括不佳。我们采用两种互补策略来解决这个问题：1）将模型暴露于中间培训序列中的验证策略，该训练序列更有可能在推理期间遇到，2）首先提出易于学习的课程，逐渐提高了训练样本的困难，因为该模型会变得有能力。我们展示了这些策略对两个具有挑战性的英语编辑任务的功效：可控文本简化和抽象性摘要。我们的方法可显着提高任务的输出质量，并在简化任务上更好地提高输出复杂性。
We propose a framework for training non-autoregressive sequence-to-sequence models for editing tasks, where the original input sequence is iteratively edited to produce the output. We show that the imitation learning algorithms designed to train such models for machine translation introduces mismatches between training and inference that lead to undertraining and poor generalization in editing scenarios. We address this issue with two complementary strategies: 1) a roll-in policy that exposes the model to intermediate training sequences that it is more likely to encounter during inference, 2) a curriculum that presents easy-to-learn edit operations first, gradually increasing the difficulty of training samples as the model becomes competent. We show the efficacy of these strategies on two challenging English editing tasks: controllable text simplification and abstractive summarization. Our approach significantly improves output quality on both tasks and controls output complexity better on the simplification task.
