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Found 8 papers, 5 papers with code
RobBERT-2022: Updating a Dutch Language Model to Account for Evolving Language Use
To evaluate if our new model is a plug-in replacement for RobBERT, we introduce two additional criteria based on concept drift of existing tokens and alignment for novel tokens. We found that for certain language tasks this update results in a significant performance increase.
RobBERTje: a Distilled Dutch BERT Model
We found that the performance of the models using the shuffled versus non-shuffled datasets is similar for most tasks and that randomly merging subsequent sentences in a corpus creates models that train faster and perform better on tasks with long sequences.
Shape Inference and Grammar Induction for Example-based Procedural Generation
Designers increasingly rely on procedural generation for automatic generation of content in various industries.
DeepStochLog: Neural Stochastic Logic Programming
Like graphical models, these probabilistic logic programs define a probability distribution over possible worlds, for which inference is computationally hard.
Dutch Humor Detection by Generating Negative Examples
Detecting if a text is humorous is a hard task to do computationally, as it usually requires linguistic and common sense insights.
Discovering Textual Structures: Generative Grammar Induction using Template Trees
In this paper, we introduce a novel grammar induction algorithm for learning interpretable grammars for generative purposes, called Gitta.
RobBERT: a Dutch RoBERTa-based Language Model
Training a Dutch BERT model thus has a lot of potential for a wide range of Dutch NLP tasks.
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Generating Philosophical Statements using Interpolated Markov Models and Dynamic Templates
Automatically imitating input text is a common task in natural language generation, often used to create humorous results.
