Context Dependent Semantic Parsing: A Survey

By Zhuang Li, Lizhen Qu, Gholamreza Haffari. 2020

22 citations recorded by the source corpus.

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 the contextual information (e.g. dialogue and comments history), which has a great potential to boost the semantic parsing systems. 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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Lizhen Qu
2. Gholamreza Haffari
3. Zhuang Li

In Computer Vision and Pattern Recognition

1. Lizhen Qu
2. Gholamreza Haffari
3. Zhuang Li

Citations in the corpus

• Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers (2021, background): “Semantic Parsing The recent surveys (Kamath and Das, 2018; Zhu et al., 2019; Li et al., 2020) cover an ample of work in semantic parsing.”