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Language models.
Recurrent neural networks have been applied to estimate language models.
We used the IRSTLM toolkit [20] for training language models.
Statistical language models can be estimated based on various approaches.
Figure 6 Structure of the language models mappers.
Table 4 shows the results of various language models.
A language models with low perplexity indicate more predictable language.
The final active hypotheses are rescored using language models.
Neural network-based language models offer several advantages.
All of which means Google can train its language models relatively quickly.
To do this, the company tells me, it's using pre-trained language models for data classification.
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