Spectral Learning of Sequence Taggers over Continuous Sequences.

In this paper we present a spectral algorithm for learning weighted finite-state sequence taggers (WFSTs) over paired input-output sequences, where the input is continuous and the output discrete. WFSTs are an important tool for modelling paired input-output sequences and have numerous applications in real-world problems. Our approach is based on generalizing the class of weighted finite-state sequence taggers over discrete input-output sequences to a class where transitions are linear combinations of elementary transitions and the weights of the linear combination are determined by dynamic features of the continuous input sequence. The resulting learning algorithm is efficient and accurate. This work has been partially funded by: the European Commission for project XLike (FP7-288342); the Spanish Government for project BASMATI (TIN2011-27479-C04-03); and the ERA-Net CHISTERA project VISEN.