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Found 7 papers, 2 papers with code
ADAPT at SR’20: How Preprocessing and Data Augmentation Help to Improve Surface Realization
In this paper, we describe the ADAPT submission to the Surface Realization Shared Task 2020.
How to Make Neural Natural Language Generation as Reliable as Templates in Task-Oriented Dialogue
Neural Natural Language Generation (NLG) systems are well known for their unreliability.
Shape of synth to come: Why we should use synthetic data for English surface realization
The Surface Realization Shared Tasks of 2018 and 2019 were Natural Language Generation shared tasks with the goal of exploring approaches to surface realization from Universal-Dependency-like trees to surface strings for several languages.
Designing a Symbolic Intermediate Representation for Neural Surface Realization
Generated output from neural NLG systems often contain errors such as hallucination, repetition or contradiction.
E2E NLG Challenge Submission: Towards Controllable Generation of Diverse Natural Language
In natural language generation (NLG), the task is to generate utterances from a more abstract input, such as structured data.
End-to-End Content and Plan Selection for Data-to-Text Generation
Learning to generate fluent natural language from structured data with neural networks has become an common approach for NLG.
Generating High-Quality Surface Realizations Using Data Augmentation and Factored Sequence Models
This work presents a new state of the art in reconstruction of surface realizations from obfuscated text.
