It differs in the following respects: (a) it does not artificially restrict the depth of the dependencies it considers and can thus be viewed as an infinite order dependency language model; (b) it not only estimates the probability of a string but is also capable of generating dependency trees; (c) finally, contrary to previous dependencybased language models which encode syntactic in-formation as features, our model takes tree structure into account more directly via representing different types of dependency edges explicitly using LSTMs.
there is no need to manually determine which dependency tree features should be used or how large the feature embeddings should be.