Papers › Improved Differentiable Architecture Search for Language Modeling and Named Entity Recognition
Improved Differentiable Architecture Search for Language Modeling and Named Entity Recognition
Yufan Jiang, Chi Hu, Tong Xiao, Chunliang Zhang, Jingbo Zhu
In this paper, we study differentiable neural architecture search (NAS) methods for natural language processing. In particular, we improve differentiable architecture search by removing the softmax-local constraint. Also, we apply differentiable NAS to named entity recognition (NER). It is the first time that differentiable NAS methods are adopted in NLP tasks other than language modeling. On both the PTB language modeling and CoNLL-2003 English NER data, our method outperforms strong baselines. It achieves a new state-of-the-art on the NER task.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Language Modelling | PTB Diagnostic ECG Database | I-DARTS | PPL | 56.0 | #1 of 1 | Archive leaderboard | report |
| Named Entity Recognition (NER) | CoNLL 2003 (English) | I-DARTS + Flair | F1 | 93.47 | #21 of 73 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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