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Conditional Random Fields (CRF) are frequently applied for labeling andsegmenting sequence data. Morency et al. (2007) introduced hidden statevariables in a labeled CRF structure in order to model the latent dynamicswithin class labels, thus improving the labeling performance. Such a model isknown as Latent-Dynamic CRF (LDCRF). We present Factored LDCRF (FLDCRF), astructure that allows multiple latent dynamics of the class labels to interactwith each other. Including such latent-dynamic interactions leads to improvedlabeling performance on single-label and multi-label sequence modeling tasks.We apply our FLDCRF models on two single-label (one nested cross-validation)and one multi-label sequence tagging (nested cross-validation) experimentsacross two different datasets - UCI gesture phase data and UCI opportunitydata. FLDCRF outperforms all state-of-the-art sequence models, i.e., CRF,LDCRF, LSTM, LSTM-CRF, Factorial CRF, Coupled CRF and a multi-label LSTM modelin all our experiments. In addition, LSTM based models display inconsistentperformance across validation and test data, and pose diffculty to selectmodels on validation data during our experiments. FLDCRF offers easier modelselection, consistency across validation and test performance and lucid modelintuition. FLDCRF is also much faster to train compared to LSTM, even without aGPU. FLDCRF outshines the best LSTM model by ~4% on a single-label task on UCIgesture phase data and outperforms LSTM performance by ~2% on average acrossnested cross-validation test sets on the multi-label sequence taggingexperiment on UCI opportunity data. The idea of FLDCRF can be extended to joint(multi-agent interactions) and heterogeneous (discrete and continuous) statespace models.

Factored Latent-Dynamic Conditional Random Fields for Single and Multi-label Sequence Modeling