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.

本研究提出了 AdaND 模型，通过上下文智能生成不同的编码器 - 解码器参数，包含上下文感知和主题感知两种自适应参数机制。在大规模实际对话数据集上进行的大量实验表明，我们的模型在定量和定性评价方面均取得了更好的性能。