Learning to Map into a Universal POS Tagset
Yuan Zhang, Roi Reichart, Regina Barzilay, Amir Globerson · 2012
We present an automatic method for mapping language-specific part-of-speech tags to a set of universal tags. This unified representation plays a crucial role in cross-lingual syntactic transfer of multilingual dependency parsers. Until now, however, such conversion schemes have been created manually. Our central hy-pothesis is that a valid mapping yields POS annotations with coherent linguistic proper-ties which are consistent across source and target languages. We encode this intuition in an objective function that captures a range of distributional and typological characteris-tics of the derived mapping. Given the ex-ponential size of the mapping space, we pro-pose a novel method for optimizing over soft mappings, and use entropy regularization to drive those towards hard mappings. Our re-sults demonstrate that automatically induced mappings rival the quality of their manually designed counterparts when evaluated in the context of multilingual parsing.1 1