  We demonstrate that an attention-based encoder-decoder model can be used for
sentence-level grammatical error identification for the Automated Evaluation of
Scientific Writing (AESW) Shared Task 2016. The attention-based encoder-decoder
models can be used for the generation of corrections, in addition to error
identification, which is of interest for certain end-user applications. We show
that a character-based encoder-decoder model is particularly effective,
outperforming other results on the AESW Shared Task on its own, and showing
gains over a word-based counterpart. Our final model--a combination of three
character-based encoder-decoder models, one word-based encoder-decoder model,
and a sentence-level CNN--is the highest performing system on the AESW 2016
binary prediction Shared Task.
