We further refine CHMM with an alternate-training approach (CHMM-ALT). ![]() Specifically, CHMM learns token-wise transition and emission probabilities from the BERT embeddings of the input tokens to infer the latent true labels from noisy observations. CHMM enhances the classic hidden Markov model with the contextual representation power of pre-trained language models. To address this challenge, we propose a conditional hidden Markov model (CHMM), which can effectively infer true labels from multi-source noisy labels in an unsupervised way. Though cheap to obtain, the labels from weak supervision sources are often incomplete, inaccurate, and contradictory, making it difficult to learn an accurate NER model. Abstract We study the problem of learning a named entity recognition (NER) tagger using noisy labels from multiple weak supervision sources.
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