# What are the main differences between a language model and a machine translation model?

What are the main differences between a language model and a machine translation model?

The simple language model will give you the probability of a sequence of tokens(sentence) for that language. So lets say if you have trained a model for English language your model can give you the probability for any random english sentence.

Consider some sentence

$$X$$ $$=$$ "the quick brown fox jumps over the lazy dog" $$=$$ $$x_1 \ x_2 \ x_3 \ ... \ x_n$$

model will give you $$P(X)$$

Moreover if the model has been trained properly, in the following scenario, where

$$X$$ $$=$$ "the quick brown fox jumps over the lazy dog" $$=$$ $$x_1 \ x_2 \ x_3 \ ... \ x_n$$

$$Y$$ $$=$$ "dog brown quick fox over the jumps lazy the" $$=$$ $$y_1 \ y_2 \ y_3 \ ... \ y_m$$

model will always give $$P(X) > P(Y)$$, as it has learnt the structure of language.

On the other hand Machine translation model gives you the conditional probability of the next token given your source sentence and partial-target sentence. So if

$$X =$$ "I am a student" and $$Y =$$ "je suis" $$=y_1, \ y_2$$

model will give you $$P(y_3 | X,y1_,y_2)$$ .

where $$X$$ is a source sentence and $$Y= y_1, y_2$$ is a partial target sentence. The probability of word/token "étudiant" would be maximum among all words of vocabulary.