@inproceedings{8c14cde4a6f74ee882f89c34e8c4b942,
title = "Training with exploration improves a greedy stack LSTM parser",
abstract = "We adapt the greedy stack LSTM dependency parser of Dyer et al. (2015) to support a training-with-exploration procedure using dynamic oracles (Goldberg and Nivre, 2013) instead of assuming an error-free action history. This form of training, which accounts for model predictions at training time, improves parsing accuracies. We discuss some modifications needed in order to get training with exploration to work well for a probabilistic neural network dependency parser.",
author = "Miguel Ballesteros and Yoav Goldberg and Chris Dyer and Smith, {Noah A.}",
note = "Publisher Copyright: {\textcopyright} 2016 Association for Computational Linguistics; 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016 ; Conference date: 01-11-2016 Through 05-11-2016",
year = "2016",
doi = "https://doi.org/10.18653/v1/d16-1211",
language = "الإنجليزيّة",
series = "EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings",
publisher = "Association for Computational Linguistics (ACL)",
pages = "2005--2010",
booktitle = "EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings",
address = "الولايات المتّحدة",
}