Abstract
This paper describes the system of our team Phoenix for participating CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. Given the annotated gold standard data in CoNLL-U format, we train the tokenizer, tagger and parser separately for each treebank based on an open source pipeline tool UDPipe. Our system reads the plain texts for input, performs the pre-processing steps (tokenization, lemmas, morphology) and finally outputs the syntactic dependencies. For the low-resource languages with no training data, we use cross-lingual techniques to build models with some close languages instead. In the official evaluation, our system achieves the macro-averaged scores of 65.61%, 52.26%, 55.71% for LAS, MLAS and BLEX respectively.- Anthology ID:
- K18-2007
- Volume:
- Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
- Month:
- October
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Daniel Zeman, Jan Hajič
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 74–80
- Language:
- URL:
- https://aclanthology.org/K18-2007
- DOI:
- 10.18653/v1/K18-2007
- Cite (ACL):
- Yingting Wu, Hai Zhao, and Jia-Jun Tong. 2018. Multilingual Universal Dependency Parsing from Raw Text with Low-Resource Language Enhancement. In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 74–80, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Multilingual Universal Dependency Parsing from Raw Text with Low-Resource Language Enhancement (Wu et al., CoNLL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/K18-2007.pdf
- Data
- Universal Dependencies