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Pattern Analysis and Applications
Erschienen in: Pattern Analysis and Applications 3/2015

01.08.2015 | Theoretical Advances

Minimum Bayes’ risk subsequence combination for machine translation

verfasst von: Jesús González-Rubio, Francisco Casacuberta

Erschienen in: Pattern Analysis and Applications | Ausgabe 3/2015

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Abstract

System combination has proved to be a successful technique in the pattern recognition field. However, several difficulties arise when combining the outputs of tasks, e.g. machine translation, that generates structured patterns. So far, machine translation system combination approaches either implement sophisticated classifiers to select one of the provided translations, or generate new sentences by combining the “best” subsequences of the provided translations. We present minimum Bayes’ risk system combination (MBRSC), a system combination method for machine translation that gathers together the advantages of sentence-selection and subsequence-combination methods. MBRSC is able to detect and utilize the “best” subsequences of the provided translations to generate the optimal consensus translation with respect to a particular performance metric. Experiments show that MBRSC obtains significant improvements in translation quality, and a particularly competitive performance when applied to languages with scarce resources.
Vorheriger Artikel Omnifont text recognition of printed cursive scripts via HMMs, compact lossless features, and soft data clustering
Nächster Artikel Discriminative learning of generative models: large margin multinomial mixture models for document classification

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Fußnoten
1
We will refer as \(n\)-gram to a sequence of \(n\) consecutive words in a sentence.
 
2
\(Pr(\cdot )\) denotes general probability distributions, \(P(\cdot )\) denotes model-based distributions, and \(\mathbb {E}_{Pr(X)}[X]\) denotes the expected value of a random variable \(X\) under distribution \(Pr(X)\).
 
3
The brevity penalty is also a function of \(n\)-gram counts: \(|{{\mathrm{\mathbf {y}}}}'|=\sum _{{{\mathrm{\mathbf {w}}}}\in {{\mathrm{\mathcal {W}}}}_1({{\mathrm{\mathbf {y}}}}')}\#_{{{\mathrm{\mathbf {w}}}}}({{\mathrm{\mathbf {y}}}}')\).
 
4
This can be done straightforwardly if the domain of translations is represented as a list. For more complex graph-based representations, we can use the algorithms proposed in [10, 11, 26].
 
5
Following the definition of the BLEU score (see previous section), we take into consideration \(n\)-grams up to size four.
 
6
The number is computed by the multiset coefficient [42] and it is exponential in the size of the target vocabulary.
 
7
The BLEU-based score cannot be computed incrementally due to the \(\text{ min }(\cdot )\) functions in its formulation.
 
9
Similarly as done in [2], we give \(p\) values on a logarithmic scale. Note that \(10^{-4}\) is the smallest possible \(p\) value that can be computed with \(9,999\) shuffles in the randomized test.
 
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Metadaten
Titel
Minimum Bayes’ risk subsequence combination for machine translation
verfasst von
Jesús González-Rubio
Francisco Casacuberta
Publikationsdatum
01.08.2015
Verlag
Springer London
Erschienen in
Pattern Analysis and Applications / Ausgabe 3/2015
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-014-0387-5

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