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Found 7 papers, 4 papers with code
Evaluating Text Generation from Discourse Representation Structures
We present an end-to-end neural approach to generate English sentences from formal meaning representations, Discourse Representation Structures (DRSs).
Gaining More Insight into Neural Semantic Parsing with Challenging Benchmarks
The Parallel Meaning Bank (PMB) serves as a corpus for semantic processing with a focus on semantic parsing and text generation.
Controlling Topic-Focus Articulation in Meaning-to-Text Generation using Graph Neural Networks
The idea is to add pragmatic information such as topic to the meaning representation, thereby forcing either active or passive voice when given to a natural language generation system.
Discourse Representation Structure Parsing for Chinese
Previous work has predominantly focused on monolingual English semantic parsing.
Pre-Trained Language-Meaning Models for Multilingual Parsing and Generation
Pre-trained language models (PLMs) have achieved great success in NLP and have recently been used for tasks in computational semantics.
Input Representations for Parsing Discourse Representation Structures: Comparing English with Chinese
Even with DRSs based on English, good results for Chinese are obtained.
The Parallel Meaning Bank: A Framework for Semantically Annotating Multiple Languages
This paper gives a general description of the ideas behind the Parallel Meaning Bank, a framework with the aim to provide an easy way to annotate compositional semantics for texts written in languages other than English.
