  In this work we tackle the problem of sentence boundary detection applied to
French as a binary classification task ("sentence boundary" or "not sentence
boundary"). We combine convolutional neural networks with subword-level
information vectors, which are word embedding representations learned from
Wikipedia that take advantage of the words morphology; so each word is
represented as a bag of their character n-grams.
  We decide to use a big written dataset (French Gigaword) instead of standard
size transcriptions to train and evaluate the proposed architectures with the
intention of using the trained models in posterior real life ASR
transcriptions.
  Three different architectures are tested showing similar results; general
accuracy for all models overpasses 0.96. All three models have good F1 scores
reaching values over 0.97 regarding the "not sentence boundary" class. However,
the "sentence boundary" class reflects lower scores decreasing the F1 metric to
0.778 for one of the models.
  Using subword-level information vectors seem to be very effective leading to
conclude that the morphology of words encoded in the embeddings representations
behave like pixels in an image making feasible the use of convolutional neural
network architectures.
