Consistent with recent findings (Dong and Lapata, 2016), we show that relatively simple neural sequence models are competitive with, and in some cases outperform, traditional grammar-based SP methods on bench-mark SP tasks.
this result is not observed in our technical documentation task, in part because this problem is much harder for neural learners given the sparseness of the target data and lack of redundancy.