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.
| Originalsprog | Engelsk |
|---|---|
| Titel | Proceedings of the Conference on Empirical Methods in Natural Language Processing |
| Forlag | Association for Computational Linguistics |
| Publikationsdato | 2018 |
| Status | Udgivet - 2018 |
Emneord
- Cross-lingual part-of-speech tagging
- Distant supervision
- Low-resource languages
- Morphological lexicons
- Annotation projection