@article{Yuan_Jiang_Tu_2019, title={Bidirectional Transition-Based Dependency Parsing}, volume={33}, url={https://ojs.aaai.org/index.php/AAAI/article/view/4733}, DOI={10.1609/aaai.v33i01.33017434}, abstractNote={<p>Transition-based dependency parsing is a fast and effective approach for dependency parsing. Traditionally, a transitionbased dependency parser processes an input sentence and predicts a sequence of parsing actions in a left-to-right manner. During this process, an early prediction error may negatively impact the prediction of subsequent actions. In this paper, we propose a simple framework for bidirectional transitionbased parsing. During training, we learn a left-to-right parser and a right-to-left parser separately. To parse a sentence, we perform joint decoding with the two parsers. We propose three joint decoding algorithms that are based on joint scoring, dual decomposition, and dynamic oracle respectively. Empirical results show that our methods lead to competitive parsing accuracy and our method based on dynamic oracle consistently achieves the best performance.</p>}, number={01}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Yuan, Yunzhe and Jiang, Yong and Tu, Kewei}, year={2019}, month={Jul.}, pages={7434-7441} }