Summary of Dore: a Dataset For Portuguese Definition Generation, by Anna Beatriz Dimas Furtado et al.
DORE: A Dataset For Portuguese Definition Generation
by Anna Beatriz Dimas Furtado, Tharindu Ranasinghe, Frédéric Blain, Ruslan Mitkov
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces DORE, a new dataset for Definition MOdelling (DM) that fills the gap for the Portuguese language, which lacks a dedicated DM dataset despite having over 200 million native speakers. The authors propose this dataset as a valuable resource for research and study in Portuguese contexts. To evaluate the effectiveness of this dataset, several deep learning-based DM models are tested on DORE, yielding promising results. This work has significant implications for natural language processing (NLP) and Portuguese language research, enabling better understanding and applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a machine that can automatically create dictionary definitions for words. That’s the goal of definition modelling! Right now, there are datasets for many languages, but not Portuguese, which has over 200 million speakers. This paper creates a new dataset called DORE to help with this task and tests different ways of doing it using deep learning. The result is a big step forward in understanding and working with the Portuguese language. |
Keywords
* Artificial intelligence * Deep learning * Natural language processing * Nlp
