一个好的样本很难找到：神经语言生成模型的噪声注入采样和自训练
A Good Sample is Hard to Find: Noise Injection Sampling and Self-Training for Neural Language Generation Models
摘要：
深度神经网络（DNN）正在迅速成为许多自然语言生成（NLG）任务的事实上的标准建模方法。为了使这些模型真正有用，它们必须能够在测试时正确地生成新意义表示（MR）的话语。在实践中，即使是复杂的DNN与各种形式的语义控制经常无法生成忠实于输入MR的话语。在本文中，我们提出了一种架构不可知的自训练方法，以采样新的MR/文本话语对，以增加原始的训练数据。值得注意的是，在对增强数据进行训练之后，即使是具有贪心解码的简单编码器-解码器模型也能够生成语义正确的话语，这些话语在自动和人工质量评估中与最先进的输出一样好。
英文摘要：
Deep neural networks (DNN) are quickly becoming the de facto standard modeling method for many natural language generation (NLG) tasks. In order for such models to truly be useful, they must be capable of correctly generating utterances for novel meaning representations (MRs) at test time. In practice, even sophisticated DNNs with various forms of semantic control frequently fail to generate utterances faithful to the input MR. In this paper, we propose an architecture agnostic self-training method to sample novel MR/text utterance pairs to augment the original training data. Remarkably, after training on the augmented data, even simple encoder-decoder models with greedy decoding are capable of generating semantically correct utterances that are as good as state-of-the-art outputs in both automatic and human evaluations of quality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
