Distant Supervision from Disparate Sources for Low-Resource Part-of-Speech Tagging
Barbara Plank, Żeljko Agić · 2018
We introduce DSDS: a cross-lingual neural part-of-speech tagger that learns from disparate sources of distant supervision, and realistically scales to hundreds of low-resource languages.The model exploits annotation projection, instance selection, tag dictionaries, morphological lexicons, and distributed representations, all in a uniform framework.The approach is simple, yet surprisingly effective, resulting in a new state of the art without access to any gold annotated data.