Before revision:  However, CHMM enhances the  hidden Markov model 's flexibility and context representation capability by predicting token-wise transition and emission probabilities from the BERT embeddings of the input tokens . In addition, we refine CHMM 's prediction with an alternate-training approach ( CHMM-AlT ).
After revision:  However, CHMM enhances the hidden Markov model 's flexibility and context representation capability by predicting token-wise transition and emission probabilities from the BERT embeddings of the input tokens to infer the latent true labels from noisy observations. We further refine CHMM with an alternate-training approach ( CHMM-AlT ).