Abstract
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 this https URL.
Abstract (translated)
在本文中,我们旨在开发语义解析器,能够理解在与用户的交谈中嵌入的自然语言问题,并将其转化为对通用知识图(KG)中定义的正式查询。为此,我们开发了一个有数千个概念名称和关系、数百万个实体的巨大词汇表的数据集,并将用户问题进行词法分析注释,并将系统回答与执行结果对应起来。我们介绍了两种不同的语义解析方法,并突出了任务的挑战:处理巨大的词汇表、建模对话上下文、预测包含多个实体的查询,并在测试时将其 generalise 到新的问题。我们希望我们的数据集将成为开发对话语义解析器有用的测试平台。我们的数据和模型都发布在这个 https URL 上。
URL
https://arxiv.org/abs/2301.12217