Summary of Multi-bert: Leveraging Adapters and Prompt Tuning For Low-resource Multi-domain Adaptation, by Parham Abed Azad and Hamid Beigy
Multi-BERT: Leveraging Adapters and Prompt Tuning for Low-Resource Multi-Domain Adaptation
by Parham Abed Azad, Hamid Beigy
First submitted to arxiv on: 2 Apr 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 A novel approach to multi-domain name entity recognition (NER) is proposed in this paper, addressing the challenges posed by the rapid growth of textual data. The method employs a single core model with domain-specific parameters, leveraging techniques such as prompt tuning and adapters to add tailored features for each domain. Experimental results on formal and informal datasets demonstrate significant performance improvements compared to existing models, while requiring only a single instance of training and storage. This approach is particularly effective in the Persian NER setting, surpassing state-of-the-art models in some cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to recognize names and entities in different types of text. The approach uses one main model that can be adapted to different domains or styles of writing. The authors test their method on various datasets and show that it performs better than existing methods, while only needing to learn once. This makes the approach more practical for real-world applications. |
Keywords
» Artificial intelligence » Ner » Prompt
