Summary of Roberturk: Adjusting Roberta For Turkish, by Nuri Tas
RoBERTurk: Adjusting RoBERTa for Turkish
by Nuri Tas
First submitted to arxiv on: 7 Jan 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 The paper presents a pretraining approach for RoBERTa, a popular language model, using a Turkish corpora with a BPE tokenizer. The pretraining process outperforms several other models on the POS task of the BOUN dataset, but underperforms on the IMST dataset. Interestingly, the same model achieves competitive scores on the NER task of the Turkish split of the XTREME dataset, despite being trained on smaller data than its competitors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about training a special kind of computer program called RoBERTa to understand language better. They used a big collection of Turkish texts and a special way of breaking down words into smaller pieces to train it. The program performed well in some tasks, like identifying the parts of speech in sentences, but not as well in others, like recognizing named entities. Despite this, the program was still able to do well when compared to other programs trained on more data. |
Keywords
* Artificial intelligence * Language model * Ner * Pretraining * Tokenizer
