In this paper we consider the task of conversational semantic parsing over general purpose knowledge graphs (KGs) with millions of entities, and thousands of relation-types. We focus on models which are capable of interactively mapping user utterances into executable logical forms (e.g., Sparql) in the context of the conversational history. Our key idea is to represent information about an utterance and its context via a subgraph which is created dynamically, i.e., the number of nodes varies per utterance. Rather than treating the subgraph as a sequence, we exploit its underlying structure and encode it with a graph neural network which further allows us to represent a large number of (unseen) nodes. Experimental results show that dynamic context modeling is superior to static approaches, delivering performance improvements across the board (i.e., for simple and complex questions). Our results further confirm that modeling the structure of context is better at processing discourse information, (i.e., at handling ellipsis and resolving coreference) and longer interactions.

翻译：本文考虑在包含数百万实体和数千种关系类型的通用知识图谱上的对话式语义解析任务。我们聚焦于能够在对话历史上下文中，将用户话语交互式映射为可执行逻辑形式（如SPARQL）的模型。核心思想是通过动态创建的子图来表示话语及其上下文信息——即每个话语对应的节点数量动态变化。不同于将子图视为序列，我们利用其底层结构，通过图神经网络进行编码，从而能够表征大量（未见过的）节点。实验结果表明，动态上下文建模优于静态方法，在各类问题上（包括简单与复杂问题）均带来性能提升。结果进一步证实，对上下文结构进行建模能更有效地处理话语信息（如省略处理和共指消解）以及更长的交互过程。