Improved Differentiable Architecture Search for Language Modeling and Named Entity Recognition (D19-1)
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| Challenge: | Neural architecture search (NAS) is a popular approach for finding new models and freeing researchers from the hard work of designing network architectures. |
| Approach: | They propose differentiable neural architecture search methods for natural language processing . they remove the softmax-local constraint and apply it to named entity recognition . |
| Outcome: | The proposed method outperforms strong baselines on the language modeling task. |
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| Challenge: | Currently, neural models for named entity recognition are based on data-driven models, with a strong emphasis on getting rid of the efforts for collecting external resources or designing hand-crafted features. |
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| Challenge: | Named Entity Recognition (NER) is a widely adopted NLP task . authors present three variants of NER task, with dataset to support them . |
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| Challenge: | Named entity recognition is an important element of natural language understanding . a shift in focus has been on designing better neural architectures for solving NER . |
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