Context Dependent Semantic Parsing: A Survey

Abstract

Semantic parsing is the task of translating natural language utterances into machine-readable meaning representations. Currently, most semantic parsing methods are not able to utilize contextual information (e.g. dialogue and comments history), which has a great potential to boost semantic parsing performance. To address this issue, context dependent semantic parsing has recently drawn a lot of attention. In this survey, we investigate progress on the methods for the context dependent semantic parsing, together with the current datasets and tasks. We then point out open problems and challenges for future research in this area. The collected resources for this topic are available at:https://github.com/zhuang-li/Contextual-Semantic-Parsing-Paper-List.

10 pages, acceteped by COLING'2020

References in corpus (5)

• Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars
• Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation
• Coupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing
• Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base
• Context-Dependent Semantic Parsing over Temporally Structured Data