An Exploration of Left-Corner Transformations

Andreas Opedal, Eleftheria Tsipidi, Tiago Pimentel, Ryan Cotterell, Tim Vieira


Abstract
The left-corner transformation (Rosenkrantz and Lewis, 1970) is used to remove left recursion from context-free grammars, which is an important step towards making the grammar parsable top-down with simple techniques. This paper generalizes prior left-corner transformations to support semiring-weighted production rules and to provide finer-grained control over which left corners may be moved. Our generalized left-corner transformation (GLCT) arose from unifying the left-corner transformation and speculation transformation (Eisner and Blatz, 2007), originally for logic programming. Our new transformation and speculation define equivalent weighted languages. Yet, their derivation trees are structurally different in an important way: GLCT replaces left recursion with right recursion, and speculation does not. We also provide several technical results regarding the formal relationships between the outputs of GLCT, speculation, and the original grammar. Lastly, we empirically investigate the efficiency of GLCT for left-recursion elimination from grammars of nine languages. Code: https://github.com/rycolab/left-corner
Anthology ID:
2023.emnlp-main.827
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
13393–13427
Language:
URL:
https://aclanthology.org/2023.emnlp-main.827
DOI:
10.18653/v1/2023.emnlp-main.827
Bibkey:
Cite (ACL):
Andreas Opedal, Eleftheria Tsipidi, Tiago Pimentel, Ryan Cotterell, and Tim Vieira. 2023. An Exploration of Left-Corner Transformations. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 13393–13427, Singapore. Association for Computational Linguistics.
Cite (Informal):
An Exploration of Left-Corner Transformations (Opedal et al., EMNLP 2023)
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https://aclanthology.org/2023.emnlp-main.827.pdf
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