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47
Improved Alignment Models for Statistical Machine Translation
 University of Maryland, College Park, MD
, 1999
"... In this paper, we describe improved alignment models for statistical machine translation. The statistical translation approach uses two types of information: a translation model and a lan guage model. The language model used is a bigram or general mgram model. The translation model is decomp ..."
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Cited by 313 (56 self)
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In this paper, we describe improved alignment models for statistical machine translation. The statistical translation approach uses two types of information: a translation model and a lan guage model. The language model used is a bigram or general mgram model. The translation model is decomposed into a lexical and an alignment model. We describe two different approaches for statistical translation and present experimental results. The first approach is based on dependencies between single words, the second approach explicitly takes shallow phrase structures into account, using two different alignment levels: a phrase level alignment between phrases and a word level alignment between single words. We present results us ing the Verbmobil task (GermanEnglish, 6000word vocabulary) which is a limiteddomain spokenlanguage task. The experimental tests were performed on both the text transcription and the speech recognizer output.
Fast Decoding and Optimal Decoding for Machine Translation
 In Proceedings of ACL 39
, 2001
"... A good decoding algorithm is critical ..."
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Decoding Complexity in WordReplacement Translation Models
 Computational Linguistics
, 1999
"... This paper looks at decoding complexity. ..."
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Monolingual machine translation for paraphrase generation
 In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing
, 2004
"... We apply statistical machine translation (SMT) tools to generate novel paraphrases of input sentences in the same language. The system is trained on large volumes of sentence pairs automatically extracted from clustered news articles available on the World Wide Web. Alignment Error Rate (AER) is mea ..."
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Cited by 88 (5 self)
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We apply statistical machine translation (SMT) tools to generate novel paraphrases of input sentences in the same language. The system is trained on large volumes of sentence pairs automatically extracted from clustered news articles available on the World Wide Web. Alignment Error Rate (AER) is measured to gauge the quality of the resulting corpus. A monotone phrasal decoder generates contextual replacements. Human evaluation shows that this system outperforms baseline paraphrase generation techniques and, in a departure from previous work, offers better coverage and scalability than the current bestofbreed paraphrasing approaches. 1
A survey of statistical machine translation
, 2007
"... Statistical machine translation (SMT) treats the translation of natural language as a machine learning problem. By examining many samples of humanproduced translation, SMT algorithms automatically learn how to translate. SMT has made tremendous strides in less than two decades, and many popular tec ..."
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Cited by 59 (5 self)
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Statistical machine translation (SMT) treats the translation of natural language as a machine learning problem. By examining many samples of humanproduced translation, SMT algorithms automatically learn how to translate. SMT has made tremendous strides in less than two decades, and many popular techniques have only emerged within the last few years. This survey presents a tutorial overview of stateoftheart SMT at the beginning of 2007. We begin with the context of the current research, and then move to a formal problem description and an overview of the four main subproblems: translational equivalence modeling, mathematical modeling, parameter estimation, and decoding. Along the way, we present a taxonomy of some different approaches within these areas. We conclude with an overview of evaluation and notes on future directions.
Accelerated Dp Based Search For Statistical Translation
 In European Conf. on Speech Communication and Technology
, 1997
"... In this paper, we describe a fast search algorithm for statistical translation based on dynamic programming (DP) and present experimental results. The approach is based on the assumption that the word alignment is monotone with respect to the word order in both languages. To reduce the search effort ..."
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Cited by 58 (11 self)
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In this paper, we describe a fast search algorithm for statistical translation based on dynamic programming (DP) and present experimental results. The approach is based on the assumption that the word alignment is monotone with respect to the word order in both languages. To reduce the search effort for this approach, we introduce two methods: an acceleration technique to efficiently compute the dynamic programming recursion equation and a beam search strategy as used in speech recognition. The experimental tests carried out on the Verbmobil corpus showed that the search space, measured by the number of translation hypotheses, is reduced by a factor of about 230 without affecting the translation performance.
Automating knowledge acquisition for machine translation
 AI Mag
, 1997
"... How can we write a computer program to translate an English sentence into Japanese? Anyone who has taken a graduatelevel course in Arti cial Intelligence knows the answer. First, compute the meaning of the English sentence. That is, convert it into logic or your favorite knowledge ..."
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Cited by 35 (3 self)
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How can we write a computer program to translate an English sentence into Japanese? Anyone who has taken a graduatelevel course in Arti cial Intelligence knows the answer. First, compute the meaning of the English sentence. That is, convert it into logic or your favorite knowledge
Word reordering and a dynamic programming beam search algorithm for statistical machine translation
 Computational Linguistics
, 2003
"... In this article, we describe an efficient beam search algorithm for statistical machine translation based on dynamic programming (DP). The search algorithm uses the translation model presented in Brown et al. (1993). Starting from a DPbased solution to the travelingsalesman problem, we present a n ..."
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Cited by 33 (5 self)
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In this article, we describe an efficient beam search algorithm for statistical machine translation based on dynamic programming (DP). The search algorithm uses the translation model presented in Brown et al. (1993). Starting from a DPbased solution to the travelingsalesman problem, we present a novel technique to restrict the possible word reorderings between source and target language in order to achieve an efficient search algorithm. Word reordering restrictions especially useful for the translation direction German to English are presented. The restrictions are generalized, and a set of four parameters to control the word reordering is introduced, which then can easily be adopted to new translation directions. The beam search procedure has been successfully tested on the Verbmobil task (German to English, 8,000word vocabulary) and on the Canadian Hansards task (French to English, 100,000word vocabulary). For the mediumsized Verbmobil task, a sentence can be translated in a few seconds, only a small number of search errors occur, and there is no performance degradation as measured by the word error criterion used in this article. 1.
A DP based Search Algorithm for Statistical Machine Translation
, 1998
"... We introduce a novel search algorithm for statistical machine translation based on dynamic programming (DP). During the search process two statistical knowledge sources are combined: a translation model and a bigram language model. This search algorithm expands hypotheses along the positions of the ..."
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Cited by 33 (16 self)
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We introduce a novel search algorithm for statistical machine translation based on dynamic programming (DP). During the search process two statistical knowledge sources are combined: a translation model and a bigram language model. This search algorithm expands hypotheses along the positions of the target string while guaranteeing progressive coverage of the words in the source string. We present experimental results on the Verbmobil task.
Dependency tree translation: Syntactically informed phrasal smt
 In ACL
, 2005
"... done while at Microsoft Research We describe a novel approach to statistical machine translation that combines syntactic information in the source language with recent advances in phrasal translation. We depend on a sourcelanguage dependency parser and a wordaligned parallel corpus. The only targe ..."
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Cited by 24 (2 self)
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done while at Microsoft Research We describe a novel approach to statistical machine translation that combines syntactic information in the source language with recent advances in phrasal translation. We depend on a sourcelanguage dependency parser and a wordaligned parallel corpus. The only target language resource assumed is a word breaker. These are used to produce treelet (“phrase”) translation pairs as well as several models, including a channel model, an order model, and a target language model. Together these models and the treelet translation pairs provide a powerful and promising approach to MT that incorporates the power of phrasal SMT with the linguistic generality available in a parser. We evaluate two decoding approaches, one inspired by dynamic programming and the