Parsing as Tagging
Robert Vacareanu, George Caique Gouveia Barbosa, Marco A. Valenzuela-Escárcega, Mihai Surdeanu
Abstract
We propose a simple yet accurate method for dependency parsing that treats parsing as tagging (PaT). That is, our approach addresses the parsing of dependency trees with a sequence model implemented with a bidirectional LSTM over BERT embeddings, where the “tag” to be predicted at each token position is the relative position of the corresponding head. For example, for the sentence John eats cake, the tag to be predicted for the token cake is -1 because its head (eats) occurs one token to the left. Despite its simplicity, our approach performs well. For example, our approach outperforms the state-of-the-art method of (Fernández-González and Gómez-Rodríguez, 2019) on Universal Dependencies (UD) by 1.76% unlabeled attachment score (UAS) for English, 1.98% UAS for French, and 1.16% UAS for German. On average, on 12 UD languages, our method with minimal tuning performs comparably with this state-of-the-art approach: better by 0.11% UAS, and worse by 0.58% LAS.- Anthology ID:
- 2020.lrec-1.643
- Volume:
- Proceedings of the Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 5225–5231
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.643
- DOI:
- Cite (ACL):
- Robert Vacareanu, George Caique Gouveia Barbosa, Marco A. Valenzuela-Escárcega, and Mihai Surdeanu. 2020. Parsing as Tagging. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 5225–5231, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Parsing as Tagging (Vacareanu et al., LREC 2020)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2020.lrec-1.643.pdf