Abstract
Transition-based dependency parsing is a fast and effective approach for dependency parsing. Traditionally, a transition-based 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 transition-based 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.
Cite
CITATION STYLE
Yuan, Y., Jiang, Y., & Tu, K. (2019). Bidirectional transition-based dependency parsing. In 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 (pp. 7434–7441). AAAI Press. https://doi.org/10.1609/aaai.v33i01.33017434
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