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Found 8 papers, 5 papers with code
Neural Methodius Revisited: Do Discourse Relations Help with Pre-Trained Models Too?
Recent developments in natural language generation (NLG) have bolstered arguments in favor of re-introducing explicit coding of discourse relations in the input to neural models.
Self-Training for Compositional Neural NLG in Task-Oriented Dialogue
Neural approaches to natural language generation in task-oriented dialogue have typically required large amounts of annotated training data to achieve satisfactory performance, especially when generating from compositional inputs.
Predicates as Boxes in Bayesian Semantics for Natural Language
In this way we construct a model, by specifying boxes for the predicates.
Leveraging Large Pretrained Models for WebNLG 2020
In this paper, we report experiments on finetuning large pretrained models to realize resource description framework (RDF) triples to natural language.
Neural NLG for Methodius: From RST Meaning Representations to Texts
While classic NLG systems typically made use of hierarchically structured content plans that included discourse relations as central components, more recent neural approaches have mostly mapped simple, flat inputs to texts without representing discourse relations explicitly.
Two experiments for embedding Wordnet hierarchy into vector spaces
The first one leverages an existing mapping of words to feature vectors (fastText), and attempts to classify such vectors as within or outside of each class.
Generating Discourse Connectives with Pre-trained Language Models: Conditioning on Discourse Relations Helps Reconstruct the PDTB
Our results suggest that including discourse relation information in the input of the model significantly improves the consistency with which it produces a correctly realized discourse relation in the output.
Mapping the hyponymy relation of wordnet onto vector Spaces
The first one leverages an existing mapping of words to feature vectors (fasttext), and attempts to classify such vectors as within or outside of each class.
