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Data-driven approaches to machine translation (MT) achieve state-of-the-art results. Many syntax-aware approaches, such as Example-Based MT and Data-Oriented Translation, make use of tree pairs aligned at sub-sentential level. Obtaining sub-sentential alignments manually is time-consuming and error-prone, and requires expert knowledge of both source and(More)
The development of large coverage, rich unification-(constraint-) based grammar resources is very time consuming, expensive and requires lots of linguistic expertise. In this paper we report initial results on a new methodology that attempts to partially automate the development of substantial parts of large coverage, rich unification-(constraint-) based(More)
We present a method for evaluating the quality of Machine Translation (MT) output, using labelled dependencies produced by a Lexical-Functional Grammar (LFG) parser. Our dependency-based method, in contrast to most popular string-based evaluation metrics, does not unfairly penalize perfectly valid syntactic variations in the translation, and the addition of(More)
In this paper we show how labelled dependencies produced by a Lexical-Functional Grammar parser can be used in Machine Translation evaluation. In contrast to most popular evaluation metrics based on surface string comparison, our dependency-based method does not unfairly penalize perfectly valid syntactic variations in the translation, shows less bias(More)
Lexical-Functional Grammar f-structures are abstract syntactic representations approximating basic predicate-argument structure. Tree-banks annotated with f-structure information are required as training resources for stochastic versions of unification and constraint-based grammars and for the automatic extraction of such resources. In a number of papers(More)
The need for syntactically annotated data for use in natural language processing has increased dramatically in recent years. This is true especially for parallel treebanks, of which very few exist. The ones that exist are mainly hand-crafted and too small for reliable use in data-oriented applications. In this paper we introduce a novel platform for fast(More)
Deep unification-(constraint-)based grammars are usually hand-crafted. Scaling such grammars from fragments to unrestricted text is time-consuming and expensive. This problem can be exacerbated in multilingual broad-coverage grammar development scenarios. Cahill et al. (2002, 2004) and O'Donovan et al. (2004) present an automatic f-structure(More)
—Until quite recently, extending Phrase-based Statistical Machine Translation (PBSMT) with syntactic knowledge caused system performance to deteriorate. The most recent successful enrichments of PBSMT with hierarchical structure either employ non-linguistically motivated syntax for capturing hierarchical reordering phenomena, or extend the phrase(More)