Wide-Coverage Neural A* Parsing for Minimalist Grammars

John Torr, Milos Stanojevic, Mark Steedman, Shay Cohen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Minimalist Grammars (Stabler, 1997) are a computationally oriented, and rigorous formalization of many aspects of Chomsky’s (1995) Minimalist Program. This paper presents the first ever application of this formalism to the task of realistic wide-coverage parsing. The parser uses a linguistically expressive yet highly constrained grammar together with an adaptation of the A* search algorithm currently used in CCG parsing (Lewis and Steedman, 2014; Lewis et al., 2016), with supertag probabilities provided by a bi-LSTM neural network supertagger trained on MGbank, a corpus of MG derivation trees. We report on some promising initial experimental results for overall dependency recovery as well as on the recovery of certain unbounded long distance dependencies. Finally, although like other MG parsers, ours has a high order polynomial worst case time complexity, we show that in practice its expected time complexity is O(n3). The parser is publicly available.1
1: https://github.com/mgparsing/astar_mg_parser
Original languageEnglish
Title of host publicationProceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
EditorsAnna Korhonen, David Traum, Lluís Màrquez
Place of PublicationFlorence, Italy
PublisherACL Anthology
Number of pages20
Publication statusPublished - 2 Aug 2019
Event57th Annual Meeting of the Association for Computational Linguistics - Fortezza da Basso, Florence, Italy
Duration: 28 Jul 20192 Aug 2019
Conference number: 57


Conference57th Annual Meeting of the Association for Computational Linguistics
Abbreviated titleACL 2019
Internet address


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