Bitext Dependency Parsing with Bilingual Subtree Constraints

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 21–29, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Bitext Dependency Parsing with Bilingual Subtree Constraints Wenliang Chen, Jun’ichi Kazama and Kentaro Torisawa Language Infrastructure Group, MASTAR Project National Institute of Information and Communications Technology 3-5 Hikari-dai, Seika-cho, Soraku-gun, Kyoto, Japan, 619-0289 {chenwl, kazama, torisawa}@nict.go.jp Abstract This paper proposes a dependency parsing method that uses bilingual constraints to improve the accuracy of parsing bilingual texts (bitexts). In our method, a target- side tree fragment that corresponds to a source-side tree fragment is identified via word alignment and mapping rules that are automatically learned. Then it is ver- ified by checking the subtree list that is collected from large scale automatically parsed data on the target side. Our method, thus, requires gold standard trees only on the source side of a bilingual corpus in the training phase, unlike the joint parsing model, which requires gold standard trees on the both sides. Compared to the re- ordering constraint model, which requires the same training data as ours, our method achieved higher accuracy because of richer bilingual constraints. Experiments on the translated portion of the Chinese Treebank show that our system outperforms mono- lingual parsers by 2.93 points for Chinese and 1.64 points for English. 1 Introduction Parsing bilingual texts (bitexts) is crucial for train- ing machine translation systems that rely on syn- tactic structures on either the source side or the target side, or the both (Ding and Palmer, 2005; Nakazawa et al., 2006). Bitexts could provide more information, which is useful in parsing, than a usual monolingual texts that can be called “bilin- gual constraints”, and we expect to obtain more accurate parsing results that can be effectively used in the training of MT systems. With this mo- tivation, there are several studies aiming at highly accurate bitext parsing (Smith and Smith, 2004; Burkett and Klein, 2008; Huang et al., 2009). This paper proposes a dependency parsing method, which uses the bilingual constraints that we call bilingual subtree constraints and statistics concerning the constraints estimated from large unlabeled monolingual corpora. Basically, a (can- didate) dependency subtree in a source-language sentence is mapped to a subtree in the correspond- ing target-language sentence by using word align- ment and mapping rules that are automatically learned. The target subtree is verified by check- ing the subtree list that is collected from unla- beled sentences in the target language parsed by a usual monolingual parser. The result is used as additional features for the source side dependency parser. In this paper, our task is to improve the source side parser with the help of the translations on the target side. Many researchers have investigated the use of bilingual constraints for parsing (Burkett and Klein, 2008; Zhao et al., 2009; Huang et al., 2009). For example, Burkett and Klein (2008) show that parsing with joint models on bitexts im- proves performance on either or both sides. How- ever, their methods require …

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