Character-level Convolutional Networks for Text Classification

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Computing & GPUs

Cited by

1547 papers of our library cite it (of the 140673 whose bibliographies we read); 60 of them have a page here, 1487 not yet.

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• Hierarchical Attention Networks for Document Classification
• XLNet: Generalized Autoregressive Pretraining for Language Understanding
• Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach
• GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation
• QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
• Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias
• Big Bird: Transformers for Longer Sequences
• Active Example Selection for In-Context Learning
• Sentiment Analysis in the Era of Large Language Models: A Reality Check
• Calibrate Before Use: Improving Few-Shot Performance of Language Models

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.1509.01626 · arXiv 1509.01626

@misc{zhang2015character,
  title = {{Character-level Convolutional Networks for Text Classification}},
  author = {Zhang, Xiang and Zhao, Junbo and LeCun, Yann},
  year = {2015},
  doi = {10.48550/arxiv.1509.01626},
  eprint = {1509.01626},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1509.01626}
}

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