Summary of Fine-tuning Transformer-based Encoder For Turkish Language Understanding Tasks, by Savas Yildirim
Fine-tuning Transformer-based Encoder for Turkish Language Understanding Tasks
by Savas Yildirim
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this study, Transformer-based language models are explored for natural language processing tasks. The paper focuses on BERT, a popular model that has achieved state-of-the-art results on many NLU problems due to its accurate and fast fine-tuning characteristics. The authors also discuss the transfer learning capacity of these architectures, which allows pre-built models to be fine-tuned for specific tasks such as question answering. To demonstrate this, a Turkish BERT model (BERTurk) was trained with base settings and fine-tuned for various downstream tasks using the Turkish Benchmark dataset. The results show that the proposed approach outperformed existing baseline approaches for Named-Entity Recognition, Sentiment Analysis, Question Answering, and Text Classification in the Turkish Language. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses special language models to help computers understand human language better. It looks at a type of model called BERT, which is really good at understanding text. This model can be used for many different tasks, like answering questions or figuring out how people feel about something. The researchers took this model and adapted it for use with the Turkish Language. They tested it on several tasks and found that it worked better than other methods they tried. |
Keywords
» Artificial intelligence » Bert » Fine tuning » Named entity recognition » Natural language processing » Question answering » Text classification » Transfer learning » Transformer
