Inspired by the recent success of neural encoder-decoder models (Sutskever et al., 2014) for sequence-to-sequence problems in NLP, we design a neural architecture for the task.
a naïve application of the encoder-decoder model ignores much of the linguistic structure of canonical segmentation-it cannot directly model the individual canonical segments, e.g., it cannot easily produce segment-level embeddings.