@inproceedings{de4abcb963894bb0a7b261781fc2e02b,
title = "A non-monotonic Arc-Eager transition system for dependency parsing",
abstract = "Previous incremental parsers have used monotonic state transitions. However, transitions can be made to revise previous decisions quite naturally, based on further information. We show that a simple adjustment to the Arc-Eager transition system to relax its monotonicity constraints can improve accuracy, so long as the training data includes examples of mistakes for the non-monotonic transitions to repair. We evaluate the change in the context of a state-of-the-art system, and obtain a statistically significant improvement (p < 0.001) on the English evaluation and 5/10 of the CoNLL languages.",
author = "Matthew Honnibal and Yoav Goldberg and Mark Johnson",
note = "Publisher Copyright: {\textcopyright} 2013 Association for Computational Linguistics.; 17th Conference on Computational Natural Language Learning, CoNLL 2013 ; Conference date: 08-08-2013 Through 09-08-2013",
year = "2013",
language = "الإنجليزيّة",
series = "CoNLL 2013 - 17th Conference on Computational Natural Language Learning, Proceedings",
publisher = "Association for Computational Linguistics (ACL)",
pages = "163--172",
booktitle = "CoNLL 2013 - 17th Conference on Computational Natural Language Learning, Proceedings",
address = "الولايات المتّحدة",
}