In this paper, we are interested in developing semantic parsers which understand natural language questions embedded in a conversation with a user and ground them to formal queries over definitions in a general purpose knowledge graph (KG) with very large vocabularies (covering thousands of concept names and relations, and millions of entities). To this end, we develop a dataset where user questions are annotated with Sparql parses and system answers correspond to execution results thereof. We present two different semantic parsing approaches and highlight the challenges of the task: dealing with large vocabularies, modelling conversation context, predicting queries with multiple entities, and generalising to new questions at test time. We hope our dataset will serve as useful testbed for the development of conversational semantic parsers. Our dataset and models are released at https://github.com/EdinburghNLP/SPICE.

翻译：本文致力于开发语义解析器，能够理解用户对话中嵌入的自然语言问题，并将其转化为对通用知识图谱中定义的正式查询。该知识图谱包含海量词汇（涵盖数千个概念名称和关系，及数百万实体）。为此，我们构建了一个数据集，其中用户问题附带SPARQL解析标注，系统答案对应解析执行结果。我们提出了两种不同的语义解析方法，并突出该任务的挑战：处理大规模词汇、建模对话上下文、预测含多实体的查询，以及在测试阶段泛化至新问题。期望该数据集能作为对话式语义解析研究的有效测试平台。数据集与模型已发布于https://github.com/EdinburghNLP/SPICE。