Before revision:  To address such challenges , we propose a conditional hidden Markov model (CHMM) . It inherits the hidden Markov model's ability to aggregating the labels from weak sources through unsupervised learning. 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 .
After revision:  To address such challenges , we propose a conditional hidden Markov model (CHMM) , which can effectively infer true labels from multi-source noisy labels in an unsupervised way. 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 .