  This paper describes our submission (named clac) to the 2016 Discriminating
Similar Languages (DSL) shared task. We participated in the closed Sub-task 1
(Set A) with two separate machine learning techniques. The first approach is a
character based Convolution Neural Network with a bidirectional long short term
memory (BiLSTM) layer (CLSTM), which achieved an accuracy of 78.45% with
minimal tuning. The second approach is a character-based n-gram model. This
last approach achieved an accuracy of 88.45% which is close to the accuracy of
89.38% achieved by the best submission, and allowed us to rank #7 overall.
