Neural conversation systems generate responses based on the sequence-to-sequence (SEQ2SEQ) paradigm. Typically, the model is equipped with a single set of learned parameters to generate responses for given input contexts. When confronting diverse conversations, its adaptability is rather limited and the model is hence prone to generate generic responses. In this work, we propose an {\bf Ada}ptive {\bf N}eural {\bf D}ialogue generation model, \textsc{AdaND}, which manages various conversations with conversation-specific parameterization. For each conversation, the model generates parameters of the encoder-decoder by referring to the input context. In particular, we propose two adaptive parameterization mechanisms: a context-aware and a topic-aware parameterization mechanism. The context-aware parameterization directly generates the parameters by capturing local semantics of the given context. The topic-aware parameterization enables parameter sharing among conversations with similar topics by first inferring the latent topics of the given context and then generating the parameters with respect to the distributional topics. Extensive experiments conducted on a large-scale real-world conversational dataset show that our model achieves superior performance in terms of both quantitative metrics and human evaluations.

中文翻译：

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神经对话生成的自适应参数化

神经对话系统基于序列到序列 (SEQ2SEQ) 范式生成响应。通常，该模型配备了一组学习参数，以针对给定的输入上下文生成响应。在面对多样化的对话时，其适应性相当有限，因此该模型容易产生通用响应。在这项工作中，我们提出了一个 {\bf Ada}ptive {\bf N}eural {\bf}对话生成模型，\textsc{AdaND}，它通过对话特定的参数化管理各种对话。对于每个对话，模型通过参考输入上下文生成编码器-解码器的参数。特别是，我们提出了两种自适应参数化机制：上下文感知和主题感知参数化机制。上下文感知参数化通过捕获给定上下文的局部语义直接生成参数。主题感知参数化通过首先推断给定上下文的潜在主题，然后生成关于分布式主题的参数，从而在具有相似主题的对话之间实现参数共享。在大规模真实世界对话数据集上进行的大量实验表明，我们的模型在定量指标和人工评估方面都取得了卓越的性能。