Current state-of-the-art neural dialogue models learn from human conversations following the data-driven paradigm. As such, a reliable training corpus is the crux of building a robust and well-behaved dialogue model. However, due to the open-ended nature of human conversations, the quality of user-generated training data varies greatly, and effective training samples are typically insufficient while noisy samples frequently appear. This impedes the learning of those data-driven neural dialogue models. Therefore, effective dialogue learning requires not only more reliable learning samples, but also fewer noisy samples. In this paper, we propose a data manipulation framework to proactively reshape the data distribution towards reliable samples by augmenting and highlighting effective learning samples as well as reducing the effect of inefficient samples simultaneously. In particular, the data manipulation model selectively augments the training samples and assigns an importance weight to each instance to reform the training data. Note that, the proposed data manipulation framework is fully data-driven and learnable. It not only manipulates training samples to optimize the dialogue generation model, but also learns to increase its manipulation skills through gradient descent with validation samples. Extensive experiments show that our framework can improve the dialogue generation performance with respect to various automatic evaluation metrics and human judgments.

中文翻译：

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数据操作：通过学习增强和重新加权实现神经对话生成的有效实例学习

当前最先进的神经对话模型遵循数据驱动范式从人类对话中学习。因此，可靠的训练语料库是构建稳健且行为良好的对话模型的关键。然而，由于人类对话的开放性，用户生成的训练数据的质量差异很大，有效的训练样本通常不足，而嘈杂的样本经常出现。这阻碍了那些数据驱动的神经对话模型的学习。因此，有效的对话学习不仅需要更可靠的学习样本，还需要更少的噪声样本。在本文中，我们提出了一个数据操作框架，通过增加和突出有效的学习样本以及同时减少低效样本的影响，主动地将数据分布重塑为可靠的样本。特别是，数据操作模型有选择地增加训练样本并为每个实例分配重要性权重以改造训练数据。请注意，建议的数据操作框架是完全数据驱动和可学习的。它不仅操纵训练样本来优化对话生成模型，而且还学习通过使用验证样本的梯度下降来提高其操纵技能。大量实验表明，我们的框架可以提高关于各种自动评估指标和人类判断的对话生成性能。提议的数据操作框架是完全数据驱动和可学习的。它不仅操纵训练样本来优化对话生成模型，而且还学习通过使用验证样本的梯度下降来提高其操纵技能。大量实验表明，我们的框架可以提高关于各种自动评估指标和人类判断的对话生成性能。提议的数据操作框架是完全数据驱动和可学习的。它不仅操纵训练样本来优化对话生成模型，而且还学习通过使用验证样本的梯度下降来提高其操纵技能。大量实验表明，我们的框架可以提高关于各种自动评估指标和人类判断的对话生成性能。