The natural language generation (NLG) module in task-oriented dialogue systems translates structured meaning representations (MRs) into text responses, which has a great impact on users' experience as the human-machine interaction interface. However, in practice, developers often only have a few well-annotated data and confront a high data collection cost to build the NLG module. In this work, we adopt the self-training framework to deal with the few-shot MR-to-Text generation problem. We leverage the pre-trained language model to self-augment many pseudo-labeled data. To prevent the gradual drift from target data distribution to noisy augmented data distribution, we propose a novel data selection strategy to select the data that our generation model is most uncertain about. Compared with existing data selection methods, our method is: (1) parameter-efficient, which does not require training any additional neural models, (2) computation-efficient, which only needs to apply several stochastic forward passes of the model to estimate the uncertainty. We conduct empirical experiments on two benchmark datasets: FewShotWOZ and FewShotSGD, and show that our proposed framework consistently outperforms other baselines in terms of BLEU and ERR.

本文提出了一种两阶段的自我增强模型，通过模型预测不确定性筛选最具信息量的意义表示，并通过聚合每个意义表示的多个微扰潜在表示来生成准确的响应，从而生成高质量的伪标签MR-to-Text对，实验结果表明，该方法在两个基准数据集FewShotWOZ和FewShotSGD上的表现普遍优于现有的自训练方法，达到了人机一致的效果。

两阶段自增量自训练在少样本对话生成中的应用