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Language Representation Models: An Overview.
Thorben Schomacker1, Marina Tropmann-Frick1
1Department of Computer Science, Hamburg University of Applied Sciences, 20099 Hamburg, Germany.
Recent advances in neural networks and transfer learning have significantly improved natural language processing (NLP). These techniques now outperform human baselines in general language understanding evaluations.
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Area of Science:
- Computer Science
- Artificial Intelligence
- Computational Linguistics
Background:
- Text mining has evolved significantly over decades, extracting knowledge from unstructured text.
- Neural networks and deep learning have driven major advancements in Natural Language Processing (NLP).
- Recent breakthroughs in the last five years have enabled practical transfer learning applications in NLP.
Purpose of the Study:
- To provide a targeted literature review of key techniques in NLP.
- To explain the advancements that led to outperforming human baseline performance.
- To contextualize neural language models contributing to general language representation.
Main Methods:
- Targeted literature review.
- Analysis of neural network and deep learning techniques in NLP.
- Review of transfer learning methodologies in language models.
Main Results:
- Neural networks and deep learning have achieved substantial progress in NLP.
- Transfer learning techniques are now practically applicable.
- Human baseline performance in general language understanding has been surpassed.
Conclusions:
- Key techniques reviewed represent vital steps towards general language representation models.
- The practical application of transfer learning marks a significant milestone in NLP.
- Continued research in neural language models is crucial for advancing AI language capabilities.