Summary of Neural Architecture Search For Sentence Classification with Bert, by Philip Kenneweg et al.
Neural Architecture Search for Sentence Classification with BERT
by Philip Kenneweg, Sarah Schröder, Barbara Hammer
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 challenges the conventional approach in Natural Language Processing (NLP) by exploring alternative architectures for language models beyond the standard single output layer classification head. The authors employ an AutoML search to identify optimized network configurations that achieve superior performance at a relatively low computational cost. The proposed approaches are evaluated on various NLP benchmarks from the GLUE dataset, demonstrating promising results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about changing how we train language models for natural language processing tasks. Usually, we add one extra layer to the model and fine-tune it for specific jobs. But what if there’s a better way? The researchers in this study use an automated process to search for new architectures that work even better than the usual approach, but require only a little more computing power. They test their ideas on many different language processing tasks from a dataset called GLUE. |
Keywords
* Artificial intelligence * Classification * Natural language processing * Nlp
