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Results 1 - 10
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356
A new string-to-dependency machine translation algorithm with a target dependency language model
- In Proc. of ACL
, 2008
"... In this paper, we propose a novel string-todependency algorithm for statistical machine translation. With this new framework, we employ a target dependency language model during decoding to exploit long distance word relations, which are unavailable with a traditional n-gram language model. Our expe ..."
Abstract
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Cited by 135 (7 self)
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experiments show that the string-to-dependency decoder achieves 1.48 point improvement in BLEU and 2.53 point improvement in TER compared to a standard hierarchical string-tostring system on the NIST 04 Chinese-English evaluation set. 1
Soft String-to-Dependency Hierarchical Machine Translation
"... In this paper, we dissect the influence of several target-side dependency-based extensions to hierarchical machine translation, including a dependency language model (LM). We pursue a non-restrictive approach that does not prohibit the production of hypotheses with malformed dependency structures. S ..."
Abstract
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Cited by 7 (3 self)
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. Since many questions remained open from previous and related work, we offer in-depth analysis of the influence of the language model order, the impact of dependencybased restrictions on the search space, and the information to be gained from dependency tree building during decoding. The application of a
A Shift-Reduce Parsing Algorithm for Phrase-based String-to-Dependency Translation
"... We introduce a shift-reduce parsing algorithm for phrase-based string-todependency translation. As the algorithm generates dependency trees for partial translations left-to-right in decoding, it allows for efficient integration of both n-gram and dependency language models. To resolve conflicts in s ..."
Abstract
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We introduce a shift-reduce parsing algorithm for phrase-based string-todependency translation. As the algorithm generates dependency trees for partial translations left-to-right in decoding, it allows for efficient integration of both n-gram and dependency language models. To resolve conflicts
Minimum Bayes-risk decoding for statistical machine translation
- IN PROCEEDINGS OF HLT-NAACL
, 2004
"... We present Minimum Bayes-Risk (MBR) decoding for statistical machine translation. This statistical approach aims to minimize expected loss of translation errors under loss functions that measure translation performance. We describe a hierarchy of loss functions that incorporate different levels of l ..."
Abstract
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Cited by 179 (16 self)
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of linguistic information from word strings, word-to-word alignments from an MT system, and syntactic structure from parse-trees of source and target language sentences. We report the performance of the MBR decoders on a Chinese-to-English translation task. Our results show that MBR decoding can be used to tune
Tree-to-String Alignment Template for Statistical Machine Translation
, 2006
"... We present a novel translation model based on tree-to-string alignment template (TAT) which describes the alignment between a source parse tree and a target string. A TAT is capable of generating both terminals and non-terminals and performing reordering at both low and high levels. The model is lin ..."
Abstract
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Cited by 173 (32 self)
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We present a novel translation model based on tree-to-string alignment template (TAT) which describes the alignment between a source parse tree and a target string. A TAT is capable of generating both terminals and non-terminals and performing reordering at both low and high levels. The model
Efficient Incremental Decoding for Tree-to-String Translation
"... Syntax-based translation models should in principle be efficient with polynomially-sized search space, but in practice they are often embarassingly slow, partly due to the cost of language model integration. In this paper we borrow from phrase-based decoding the idea to generate a translation increm ..."
Abstract
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Cited by 16 (3 self)
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incrementally left-to-right, and show that for tree-to-string models, with a clever encoding of derivation history, this method runs in averagecase polynomial-time in theory, and lineartime with beam search in practice (whereas phrase-based decoding is exponential-time in theory and quadratic-time in practice
Improving Decoding Generalization for Tree-to-String Translation
"... Abstract To address the parse error issue for tree-tostring translation, this paper proposes a similarity-based decoding generation (SDG) solution by reconstructing similar source parse trees for decoding at the decoding time instead of taking multiple source parse trees as input for decoding. Expe ..."
Abstract
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. Experiments on Chinese-English translation demonstrated that our approach can achieve a significant improvement over the standard method, and has little impact on decoding speed in practice. Our approach is very easy to implement, and can be applied to other paradigms such as tree-to-tree models.
Left-to-Right Tree-to-String Decoding with Prediction
"... Decoding algorithms for syntax based machine translation suffer from high computational complexity, a consequence of intersecting a language model with a context free grammar. Left-to-right decoding, which generates the target string in order, can improve decoding efficiency by simplifying the langu ..."
Abstract
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Cited by 5 (1 self)
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Decoding algorithms for syntax based machine translation suffer from high computational complexity, a consequence of intersecting a language model with a context free grammar. Left-to-right decoding, which generates the target string in order, can improve decoding efficiency by simplifying
Transformation and Decomposition for Efficiently Implementing and Improving Dependency-to-String Model In Moses
"... Dependency structure provides grammat-ical relations between words, which have shown to be effective in Statistical Ma-chine Translation (SMT). In this paper, we present an open source module in Moses which implements a dependency-to-string model. We propose a method to trans-form the input dependen ..."
Abstract
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Cited by 1 (0 self)
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-structures of the dependency tree during training and creating a pseudo-forest in-stead of the tree per se as the input dur-ing decoding. Large-scale experiments on Chinese–English and German–English tasks show that the decomposition ap-proach improves the baseline dependency-to-string model significantly. Our sys
Flexible and Efficient Hypergraph Interactions for Joint Hierarchical and Forest-to-String Decoding∗
"... Machine translation benefits from system combination. We propose flexible interaction of hypergraphs as a novel technique combin-ing different translation models within one de-coder. We introduce features controlling the interactions between the two systems and ex-plore three interaction schemes of ..."
Abstract
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Cited by 3 (0 self)
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of hiero and forest-to-string models—specification, gener-alization, and interchange. The experiments are carried out on large training data with strong baselines utilizing rich sets of dense and sparse features. All three schemes signif-icantly improve results of any single system on four testsets. We
Results 1 - 10
of
356
