We combine both objectives by directly modeling the probability of a document d e in target language e given a query q f in source language f , factorized as follows: Applying the same Viterbi approximation during inference as in (1), we choose the retrieval score of d e to be the score of the highest scoring hypothesis h, where the product between both models can be interpreted as a conjunctive operation similar to a product of experts (Hinton, 2002): A high score is achieved if both experts, namely translation and retrieval models, assign high scores to a hypothesis.
the model attempts to produce a wellformed translation, but at the same time chooses lexical items present in the bag-of-words representation of the document.