Which of the following is a major limitation of traditional n-gram models compared to neural language models?<br /> A. a. High computational cost <br />B. b. Lack of generalization to unseen n-grams <br />C. c. Inability to handle variable-length sequences <br />D. d. Complexity of training
Question
Which of the following is a major limitation of traditional n-gram models compared to neural language models?<br /> A. a. High computational cost <br />B. b. Lack of generalization to unseen n-grams <br />C. c. Inability to handle variable-length sequences <br />D. d. Complexity of training
Solution
The major limitation of traditional n-gram models compared to neural language models is B. Lack of generalization to unseen n-grams.
Here's why:
Traditional n-gram models predict the next word in a sequence based on the previous 'n' words. They work well when the exact sequence of 'n' words has been seen before in the training data. However, they struggle to generalize to unseen n-grams, i.e., sequences of 'n' words that were not in the training data. This is because they treat each n-gram as a separate entity and do not share statistical strength among similar n-grams.
On the other hand, neural language models are able to generalize to unseen n-grams because they learn distributed representations of words and can share statistical strength among similar words and phrases. They can understand the semantic and syntactic similarity between different words and phrases, which allows them to make reasonable predictions even for n-
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