We have shown that autoregressive predictions expressed in the observation model instead of hidden states deliver better results on a simple corpus.
mistakes at the bigram-level, such as repetitions, are avoided and we suspect that more densely connected CRFs allow to extend these promising results to more complex patterns found in more complex corpora.