Show Some Love to Your n-grams: A Bit of Progress and Stronger n-gram Language Modeling Baselines (N19-1)
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| Challenge: | Experimental results show that standalone n-gram models lend themselves as natural choices for resource-lean or morphologically rich languages. |
| Approach: | They run experiments on 50 languages covering all morphological language families to compare n-gram models with lstm models. |
| Outcome: | The proposed extension outperforms an lstm language model on 42 languages while its extension which explicitly injects linguistic knowledge outperformed the character-aware neural model on 8 languages. |
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