We evaluated the performance of various transfer learning methods on the data and tasks described in §3.1.
we compared our approximate hierarchical prior model (HIER), implemented as a CRF, against three baselines: • GAUSS: CRF model tuned on a single domain's data, using a standard N (0, 1) prior • CAT: CRF model tuned on a concatenation of multiple domains' data, using a N (0, 1) prior • CHELBA: CRF model tuned on one domain's data, using a prior trained on a different, related domain's data (cf.