Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Conference on Empirical Methods in Natural Language Processing |
| Publisher | Association for Computational Linguistics |
| Publication date | 2018 |
| Publication status | Published - 2018 |
Keywords
- Cross-lingual part-of-speech tagging
- Distant supervision
- Low-resource languages
- Morphological lexicons
- Annotation projection