Decoding Algorithm in Statistical Machine Translation
Ye-Yi Wang,Alex Waibel +1 more
- pp 366-372
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TLDR
A stack decoding algorithm is described and the hypothesis scoring method and the heuristics used in the algorithm are presented, and a simplified model to moderate the sparse data problem and to speed up the decoding process is introduced.Abstract:
Decoding algorithm is a crucial part in statistical machine translation. We describe a stack decoding algorithm in this paper. We present the hypothesis scoring method and the heuristics used in our algorithm. We report several techniques deployed to improve the performance of the decoder. We also introduce a simplified model to moderate the sparse data problem and to speed up the decoding process. We evaluate and compare these techniques/models in our statistical machine translation system.read more
Citations
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Book
Foundations of Statistical Natural Language Processing
TL;DR: This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear and provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations.
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The Alignment Template Approach to Statistical Machine Translation
Franz Josef Och,Hermann Ney +1 more
TL;DR: A phrase-based statistical machine translation approach the alignment template approach is described, which allows for general many-to-many relations between words and is easier to extend than classical statistical machinetranslation systems.
Proceedings ArticleDOI
A Syntax-based Statistical Translation Model
Kenji Yamada,Kevin Knight +1 more
TL;DR: This model transforms a source-language parse tree into a target-language string by applying stochastic operations at each node, and produces word alignments that are better than those produced by IBM Model 5.
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Decoding complexity in word-replacement translation models
TL;DR: This work shows that for the simplest form of statistical models, this problem is NP-complete, i.e., probably exponential in the length of the observed sentence, and traces this complexity to factors not present in other decoding problems.
Proceedings ArticleDOI
Fast Decoding and Optimal Decoding for Machine Translation
TL;DR: This paper compares the speed and output quality of a traditional stack-based decoding algorithm with two new decoders: a fast greedy decoder and a slow but optimal decoder that treats decoding as an integer-programming optimization problem.
References
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The mathematics of statistical machine translation: parameter estimation
TL;DR: The authors describe a series of five statistical models of the translation process and give algorithms for estimating the parameters of these models given a set of pairs of sentences that are translations of one another.
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Problem-Solving Methods in Artificial Intelligence
TL;DR: This paper will concern you to try reading problem solving methods in artificial intelligence as one of the reading material to finish quickly.
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HMM-based word alignment in statistical translation
TL;DR: A new model for word alignment in statistical translation using a first-order Hidden Markov model for the word alignment problem as they are used successfully in speech recognition for the time alignment problem.