
摘要
许多领先的语言模型方法引入了新颖、复杂和专门化的架构。我们基于现有的基于LSTM(长短期记忆网络)和QRNN(准循环神经网络)的最先进词级语言模型,将其扩展到更大的词汇表以及字符级别的粒度。在适当调优后,LSTM和QRNN分别在字符级(Penn Treebank、enwik8)和词级(WikiText-103)数据集上取得了最先进的结果。这些结果仅使用单个现代GPU在12小时(WikiText-103)到2天(enwik8)内获得。
代码仓库
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基准测试
| 基准 | 方法 | 指标 |
|---|---|---|
| language-modelling-on-enwiki8 | AWD-LSTM (3 layers) | Bit per Character (BPC): 1.232 Number of params: 47M |
| language-modelling-on-hutter-prize | 3-layer AWD-LSTM | Bit per Character (BPC): 1.232 Number of params: 47M |
| language-modelling-on-penn-treebank-character | 6-layer QRNN | Bit per Character (BPC): 1.187 Number of params: 13.8M |
| language-modelling-on-penn-treebank-character | 3-layer AWD-LSTM | Bit per Character (BPC): 1.175 Number of params: 13.8M |
| language-modelling-on-wikitext-103 | 4 layer QRNN | Number of params: 151M Test perplexity: 33.0 Validation perplexity: 32.0 |