We propose recurrent support vector machine (RSVM) for slot tagging. This model is a combination of the recurrent neural network (RNN) and the structured support vector machine. RNN extracts features from the input sequence. The structured support vector machine uses a sequence-level discriminative objective function. The proposed model therefore combines the sequence representation capability of an RNN with the sequence-level discriminative objective. We have observed new state-ofthe-art results on two benchmark datasets and one private dataset. RSVM obtained statistical significant 4% and 2% relative average F1 score improvement on ATIS dataset and Chunking dataset, respectively. Out of eight domains in Cortana live log dataset, RSVM achieved F1 score improvement on seven domains. Experiments also show that RSVM significantly speeds up the model training by skipping the weight updating for non-support vector training samples, compared against training using RNN with CRF or minimum cross-entropy objectives.
For instance, REF propose the Recurrent Support Vector Machine (RSVM), which uses a recurrent neural network to induce a feature representation and a structured support vector machine to perform structured prediction on the output of the RNN.
Recurrent Support Vector Machines For Slot Tagging In Spoken Language Understanding
HLT-NAACL
Computer Science