Abstract
The earliest models for discontinuous constituency parsers used mildly context-sensitive grammars, but the fashion has changed in recent years to grammar-less transition-based parsers that use strong neural probabilistic models to greedily predict transitions. We argue that grammar-based approaches still have something to contribute on top of what is offered by transition-based parsers. Concretely, by using a grammar formalism to restrict the space of possible trees we can use dynamic programming parsing algorithms for exact search for the most probable tree. Previous chart-based parsers for discontinuous formalisms used probabilistically weak generative models. We instead use a span-based discriminative neural model that preserves the dynamic programming properties of the chart parsers. Our parser does not use an explicit grammar, but it does use explicit grammar formalism constraints: we generate only trees that are within the LCFRS-2 formalism. These properties allow us to construct a new parsing algorithm that runs in lower worst-case time complexity of O(l nˆ4 +nˆ6), where n is the sentence length and l is the number of unique non-terminal labels. This parser is efficient in practice, provides best results among chart-based parsers, and is competitive with the best transition based parsers. We also show that the main bottleneck for further improvement in performance is in the restriction of fan-out to degree 2. We show that well-nestedness is helpful in speeding up parsing, but lowers accuracy.- Anthology ID:
- 2020.iwpt-1.12
- Volume:
- Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Gosse Bouma, Yuji Matsumoto, Stephan Oepen, Kenji Sagae, Djamé Seddah, Weiwei Sun, Anders Søgaard, Reut Tsarfaty, Dan Zeman
- Venue:
- IWPT
- SIG:
- SIGPARSE
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 111–121
- Language:
- URL:
- https://aclanthology.org/2020.iwpt-1.12
- DOI:
- 10.18653/v1/2020.iwpt-1.12
- Cite (ACL):
- Miloš Stanojević and Mark Steedman. 2020. Span-Based LCFRS-2 Parsing. In Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies, pages 111–121, Online. Association for Computational Linguistics.
- Cite (Informal):
- Span-Based LCFRS-2 Parsing (Stanojević & Steedman, IWPT 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2020.iwpt-1.12.pdf