Factored Latent-Dynamic Conditional Random Fields for single and multi-label sequence modeling

Satyajit Neogi, Justin Dauwels

Abstract: Highlights•We propose a single and multi-label generalization of LDCRF (Morency et al., 2007), called the Factored LDCRF.•FLDCRF unifies the concepts of LDCRF and Dynamic CRFs (DCRF, Sutton et al., 2007) and extends the CRF family.•The single-label variant of FLDCRF (FLDCRF-s) outperforms state-of-the-art models, viz., CRF, LDCRF, LSTM and LSTM-CRF across 5 experiments over 2 different datasets.•The multi-label variant of FLDCRF-m outperforms state-of-the-art single-label, viz., CRF, LDCRF, LSTM and LSTM-CRF, and multi-label, viz., Coupled CRF, Factorial CRF and multi-label LSTM models on the multi-label sequence tagging experiment.•We compare FLDCRF and LSTM model families not only on the test data, but also across several other modeling aspects, e.g., model selection, consistency and computation times.