Each sentence contains very little additional data, and even if the language model is adjusted after every individual word, it only obtains evidence of previous words in the sentence, whereas these words are relatively unlikely to occur again in the same sentence.
instead of adjusting individual word representations, our approach learns a distributed document vector to represent the specific unit of text that is currently being processed.