TabGenie: A Toolkit for Table-to-Text Generation
원문 초록
Heterogenity of data-to-text generation datasets limits the research on data-to-text generation systems. We present TabGenie - a toolkit which enables researchers to explore, preprocess, and analyze a variety of data-to-text generation datasets through the unified framework of table-to-text generation. In TabGenie, all the inputs are represented as tables with associated metadata. The tables can be explored through the web interface, which also provides an interactive mode for debugging table-to-text generation, facilitates side-by-side comparison of generated system outputs, and allows easy exports for manual analysis. Furthermore, TabGenie is equipped with command line processing tools and Python bindings for unified dataset loading and processing. We release TabGenie as a PyPI package and provide its open-source code and a live demo at https://github.com/kasnerz/tabgenie.
함께 살펴볼 논문
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The Challenge of Achieving Attributability in Multilingual Table-to-Text Generation with Question-Answer Blueprints
DETQUS: Decomposition-Enhanced Transformers for QUery-focused Summarization
Making Language Model a Hierarchical Classifier and Generator
Mitigating Object Hallucinations via Sentence-Level Early Intervention
The Devil behind the mask: An emergent safety vulnerability of Diffusion LLMs
토론
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