Dependency Grammar Induction via Bitext Projection Constraints

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 369–377, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP Dependency Grammar Induction via Bitext Projection Constraints Kuzman Ganchev and Jennifer Gillenwater and Ben Taskar Department of Computer and Information Science University of Pennsylvania, Philadelphia PA, USA {kuzman,jengi,taskar}@seas.upenn.edu Abstract Broad-coverage annotated treebanks nec- essary to train parsers do not exist for many resource-poor languages. The wide availability of parallel text and accurate parsers in English has opened up the pos- sibility of grammar induction through par- tial transfer across bitext. We consider generative and discriminative models for dependency grammar induction that use word-level alignments and a source lan- guage parser (English) to constrain the space of possible target trees. Unlike previous approaches, our framework does not require full projected parses, allowing partial, approximate transfer through lin- ear expectation constraints on the space of distributions over trees. We consider several types of constraints that range from generic dependency conservation to language-specific annotation rules for aux- iliary verb analysis. We evaluate our ap- proach on Bulgarian and Spanish CoNLL shared task data and show that we con- sistently outperform unsupervised meth- ods and can outperform supervised learn- ing for limited training data. 1 Introduction For English and a handful of other languages, there are large, well-annotated corpora with a vari- ety of linguistic information ranging from named entity to discourse structure. Unfortunately, for the vast majority of languages very few linguis- tic resources are available. This situation is likely to persist because of the expense of creat- ing annotated corpora that require linguistic exper- tise (Abeillé, 2003). On the other hand, parallel corpora between many resource-poor languages and resource-rich languages are ample, motivat- ing recent interest in transferring linguistic re- sources from one language to another via parallel text. For example, several early works (Yarowsky and Ngai, 2001; Yarowsky et al., 2001; Merlo et al., 2002) demonstrate transfer of shallow pro- cessing tools such as part-of-speech taggers and noun-phrase chunkers by using word-level align- ment models (Brown et al., 1994; Och and Ney, 2000). Alshawi et al. (2000) and Hwa et al. (2005) explore transfer of deeper syntactic structure: dependency grammars. Dependency and con- stituency grammar formalisms have long coex- isted and competed in linguistics, especially be- yond English (Mel’ˇcuk, 1988). Recently, depen- dency parsing has gained popularity as a simpler, computationally more efficient alternative to con- stituency parsing and has spurred several super- vised learning approaches (Eisner, 1996; Yamada and Matsumoto, 2003a; Nivre and Nilsson, 2005; McDonald et al., 2005) as well as unsupervised in- duction (Klein and Manning, 2004; Smith and Eis- ner, 2006). Dependency representation has been used for language modeling, textual entailment and machine translation (Haghighi et al., 2005; Chelba et al., 1997; Quirk et al., 2005; Shen et al., 2008), to name a few tasks. Dependency grammars are arguably more ro- bust to transfer since syntactic relations between aligned words of parallel sentences are better con- served in translation than …

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