Recently a variety of LSTM-based conditional language models (LM) have been applied across a range of language generation tasks. In this work we study various model architectures and different ways to represent and aggregate the source information in an end-to-end neural dialogue system framework. A method called snapshot learning is also proposed to facilitate learning from supervised sequential signals by applying a companion cross-entropy objective function to the conditioning vector. The experimental and analytical results demonstrate firstly that competition occurs between the conditioning vector and the LM, and the differing architectures provide different trade-offs between the two. Secondly, the discriminative power and transparency of the conditioning vector is key to providing both model interpretability and better performance. Thirdly, snapshot learning leads to consistent performance improvements independent of which architecture is used.

本文研究了各种模型架构，不同的信息表示和聚合方法以及快照学习法在端到端神经对话系统框架中的应用，结果表明：竞争发生在条件向量和语言模型之间，不同的体系结构提供了不同的权衡，而条件向量的可辨别力和透明度对于提供模型可解释性和更好的性能至关重要。快照学习导致了一致的性能改进，无论使用哪种体系结构。

神经对话系统中的条件生成和快照学习