Summary of A Novel Cartography-based Curriculum Learning Method Applied on Ronli: the First Romanian Natural Language Inference Corpus, by Eduard Poesina et al.
A Novel Cartography-Based Curriculum Learning Method Applied on RoNLI: The First Romanian Natural Language Inference Corpus
by Eduard Poesina, Cornelia Caragea, Radu Tudor Ionescu
First submitted to arxiv on: 20 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 presents a new corpus for natural language inference (NLI) in Romanian, which is essential for building conversational agents and improving text classification. The RoNLI corpus consists of 58K training sentence pairs obtained through distant supervision and 6K manually annotated validation and test sets. The authors demonstrate competitive baselines using various machine learning methods, including shallow models based on word embeddings and transformer-based neural networks. Furthermore, they improve the best model by employing a new curriculum learning strategy based on data cartography. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a big database for computers to understand Romanian sentences better. They call it RoNLI. It has lots of sentence pairs that help computers learn about relationships between sentences. The team shows how different computer models can do well with this database, and they even come up with a new way to make the best model work even better. |
Keywords
» Artificial intelligence » Curriculum learning » Inference » Machine learning » Text classification » Transformer
