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Advantages vs. Disadvantages
Neural language models have many advantages over the n-gram language models. Compared to n-gram models, neural language models can handle much longer histories, can generalize better over contexts of similar words, and are more accurate at word-prediction.
On the other hand, neural net language models are much more complex, slower to train, and less interpretable than n-gram models, so for many (especially smaller) tasks an n-gram language model is still the right tool.
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Mini example
Bengio et al. (2003) Feed-Forward Neural Language Model
A language model is designed using a feedforward network architecture. It is trained to predict the next word by looking at a fixed-size window of the N preceding words (e.g., N=4). What is the most significant architectural limitation of this approach for modeling language?
Consider a feedforward neural network designed to predict the next word based on a fixed window of the three preceding words. Arrange the following computational steps in the correct order, from initial input to final output.
Function of Word Vector Representations
Related
Mini example
Bengio et al. (2003) Feed-Forward Neural Language Model
A language model is designed using a feedforward network architecture. It is trained to predict the next word by looking at a fixed-size window of the N preceding words (e.g., N=4). What is the most significant architectural limitation of this approach for modeling language?
Consider a feedforward neural network designed to predict the next word based on a fixed window of the three preceding words. Arrange the following computational steps in the correct order, from initial input to final output.
Function of Word Vector Representations