IBM Research at the CoNLL 2018 Shared Task on Multilingual Parsing
Hui Wan, Tahira Naseem, Young-Suk Lee, Vittorio Castelli, Miguel Ballesteros
Abstract
This paper presents the IBM Research AI submission to the CoNLL 2018 Shared Task on Parsing Universal Dependencies. Our system implements a new joint transition-based parser, based on the Stack-LSTM framework and the Arc-Standard algorithm, that handles tokenization, part-of-speech tagging, morphological tagging and dependency parsing in one single model. By leveraging a combination of character-based modeling of words and recursive composition of partially built linguistic structures we qualified 13th overall and 7th in low resource. We also present a new sentence segmentation neural architecture based on Stack-LSTMs that was the 4th best overall.- Anthology ID:
- K18-2009
- 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:
- 92–102
- Language:
- URL:
- https://aclanthology.org/K18-2009
- DOI:
- 10.18653/v1/K18-2009
- Cite (ACL):
- Hui Wan, Tahira Naseem, Young-Suk Lee, Vittorio Castelli, and Miguel Ballesteros. 2018. IBM Research at the CoNLL 2018 Shared Task on Multilingual Parsing. In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 92–102, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- IBM Research at the CoNLL 2018 Shared Task on Multilingual Parsing (Wan et al., CoNLL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/K18-2009.pdf
- Data
- Universal Dependencies