Distant Supervision from Disparate Sources for Low-Resource Part-of-Speech Tagging
Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review
We introduce DSDS: a cross-lingual neural part-of-speech tagger that learns from dis- parate 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.
|Title of host publication||Proceedings of the Conference on Empirical Methods in Natural Language Processing|
|Publisher||Association for Computational Linguistics|
|Publication status||Published - 2018|