In our case, the input is not a natural language source sentence as in traditional machine translation; instead, the input x 1:S is a meaning representation, where each token x n is itself a tuple of attribute and value features, (f attr , f val ).
we represent a given input x 1:S as a sequence of attribute-value pairs from an input MR. For example, in the case of BASE MR [(attr=food, val=steak), (attr=food, val=chicken)], we would have , and x 2 =(f attr =food,f val =chicken).