Shift-reduce Spinal TAG Parsing with Dynamic Programming

• Published in: Transactions of the Japanese Society for Artificial Intelligence Vol. 31; no. 2; pp. J-F83_1 - 8
• Main Authors: Hayashi, Katsuhiko, Suzuki, Jun, Nagata, Masaaki
• Format: Journal Article
• Language: English
• Published: Tokyo The Japanese Society for Artificial Intelligence 01.03.2016; Japan Science and Technology Agency
• Subjects: dynamic programming; spinal tree adjoining grammar; supertagging; transition-based parsing
• ISSN: 1346-0714; 1346-8030
• DOI: 10.1527/tjsai.J-F83

The spinal tree adjoining grammar (TAG) parsing model of [Carreras 08] achieves the current state-of-the-art constituent parsing accuracy on the commonly used English Penn Treebank evaluation setting. Unfortunately, the model has the serious drawback of low parsing efficiency since its Eisner-CKY style parsing algorithm needs O(n4) computation time for input length n. This paper investigates a more practical solution and presents a beam search shift-reduce algorithm for spinal TAG parsing. Since the algorithm works in O(bn) (b is beam width), it can be expected to provide a significant improvement in parsing speed. However, to achieve faster parsing, it needs to prune a large number of candidates in an exponentially large search space and often suffers from severe search errors. In fact, our experiments show that the basic beam search shift-reduce parser does not work well for spinal TAGs. To alleviate this problem, we extend the proposed shift-reduce algorithm with two techniques: Dynamic Programming of [Huang 10a] and Supertagging. The proposed extended parsing algorithm is about 8 times faster than the Berkeley parser, which is well-known to be fast constituent parsing software, while offering state-of-the-art performance. Moreover, we conduct experiments on the Keyaki Treebank for Japanese to show that the good performance of our proposed parser is language-independent.