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Found 6 papers, 4 papers with code
German Text Simplification: Finetuning Large Language Models with Semi-Synthetic Data
This study pioneers the use of synthetically generated data for training generative models in document-level text simplification of German texts.
Explaining Relation Classification Models with Semantic Extents
We introduce semantic extents, a concept to analyze decision patterns for the relation classification task.
ALE: A Simulation-Based Active Learning Evaluation Framework for the Parameter-Driven Comparison of Query Strategies for NLP
Supervised machine learning and deep learning require a large amount of labeled data, which data scientists obtain in a manual, and time-consuming annotation process.
Multi-Attribute Relation Extraction (MARE) -- Simplifying the Application of Relation Extraction
Extracting relations with an arbitrary amount of attributes requires complex systems and costly relation-trigger annotations to assist these systems.
STAMP 4 NLP -- An Agile Framework for Rapid Quality-Driven NLP Applications Development
The progress in natural language processing (NLP) research over the last years, offers novel business opportunities for companies, as automated user interaction or improved data analysis.
NLP Lean Programming Framework: Developing NLP Applications More Effectively
This paper presents NLP Lean Programming framework (NLPf), a new framework for creating custom Natural Language Processing (NLP) models and pipelines by utilizing common software development build systems.
