Neural community centered language models simplicity the sparsity issue Incidentally they encode inputs. Word embedding levels generate an arbitrary sized vector of each phrase that comes with semantic interactions as well. These constant vectors build the Considerably desired granularity while in the probability distribution of the subsequent word