著者

Dan Han Pascual Martínez-Gómez Yusuke Miyao Katsuhito Sudoh Masaaki Nagata

出版者

Information and Media Technologies Editorial Board

雑誌

Information and Media Technologies (ISSN:18810896)

巻号頁・発行日

vol.9, no.3, pp.272-301, 2014 (Released:2014-09-15)

参考文献数

44

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In statistical machine translation, Chinese and Japanese is a well-known long-distance language pair that causes difficulties to word alignment techniques. Pre-reordering methods have been proven efficient and effective; however, they need reliable parsers to extract the syntactic structure of the source sentences. On one hand, we propose a framework in which only part-of-speech (POS) tags and unlabeled dependency parse trees are used to minimize the influence of parse errors, and linguistic knowledge on structural difference is encoded in the form of reordering rules. We show significant improvements in translation quality of sentences in the news domain over state-of-the-art reordering methods. On the other hand, we explore the relationship between dependency parsing and our pre-reordering method from two aspects: POS tags and dependencies. We observe the effects of different parse errors on reordering performance by combining empirical and descriptive approaches. In the empirical approach, we quantify the distribution of general parse errors along with reordering quality. In the descriptive approach, we extract seven influential error patterns and examine their correlations with reordering errors.

著者

Katsuhiko Hayashi Jun Suzuki Masaaki Nagata

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

pp.J-F83, (Released:2015-12-17)

参考文献数

24

被引用文献数

1

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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.