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We investigate encoder-decoder GRU networks with attention mechanism for solving a diverse array of elementary math problems with mathematical symbolic structures. We quantitatively measure performances of recurrent models on a given question type using a test set of unseen problems with a binary scoring and partial credit system. From our findings, we propose the use of encoder-decoder recurrent neural networks for the generation of mathematical multiple-choice question distractors. We introduce a computationally inexpensive decoding schema called character offsetting, which qualitatively and quantitatively shows promise for doing so for several question types. Character offsetting involves freezing the hidden state and top k probabilities of a decoder's initial probability outputs given the input of an encoder, then performing k basic greedy decodings given each of the frozen outputs as the initialization for decoded sequence. [For the full proceedings, see ED615472.]