  This paper proposes a Transformer-based model to generate equations for math
word problems. It achieves much better results than RNN models when copy and
align mechanisms are not used, and can outperform complex copy and align RNN
models. We also show that training a Transformer jointly in a generation task
with two decoders, left-to-right and right-to-left, is beneficial. Such a
Transformer performs better than the one with just one decoder not only because
of the ensemble effect, but also because it improves the encoder training
procedure. We also experiment with adding reinforcement learning to our model,
showing improved performance compared to MLE training.
