Papers with hidden Markov model
Transformer-Based Direct Hidden Markov Model for Machine Translation (2021.acl-srw)
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| Challenge: | Recent studies have found that word alignments produced by the multi-head cross-attention weights are poor. |
| Approach: | They propose to introduce the hidden Markov model to the transformer architecture and introduce alignment components while keeping the system monolithic. |
| Outcome: | The proposed model outperforms the baseline model but is slower in training and decoding. |
Scaling Hidden Markov Language Models (2020.emnlp-main)
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| Challenge: | Hidden Markov models are a fundamental tool for sequence modeling that separates the hidden state from the emission structure. |
| Approach: | They propose methods for scaling hidden Markov models to massive state spaces while maintaining efficient exact inference and effective regularization. |
| Outcome: | The proposed methods are much more accurate than previous HMMs and n-gram-based methods, making progress towards the performance of state-of-the-art NN models. |
Named Entity Recognition without Labelled Data: A Weak Supervision Approach (2020.acl-main)
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| Challenge: | Named Entity Recognition (NER) performance often degrades when applied to target domains that differ from the texts observed during training. |
| Approach: | They propose a method to learn NER models in the absence of labelled data through weak supervision by using a broad spectrum of labelling functions to automatically annotate texts from the target domain. |
| Outcome: | The proposed approach improves on two English datasets and shows that it improves by 7 percentage points on entity-level F1 scores compared to an out-of-domain neural NER model. |
BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition (2021.acl-long)
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| Challenge: | Existing NER models are supervised by a large number of training sequences, each pre-annotated with token-level labels. |
| Approach: | They propose a conditional hidden Markov model which can effectively infer true labels from multi-source noisy labels in an unsupervised way. |
| Outcome: | The proposed model outperforms state-of-the-art weakly supervised NER models on four benchmarks from various domains. |