Multi-engine machine translation with an open-source decoder for statistical machine translation

Chen, Yu; Eisele, Andreas; Federmann, Christian; Hasler, Eva; Jellinghaus, Michael; Theison, Silke

doi:10.3115/1626355.1626381

Cited by 13 publications

(9 citation statements)

References 13 publications

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“…Different approaches have been proposed and experiments conducted to combine results from multiple systems (Nirenburg & Frederlcing, 1994;Tidhar & Kiissner, 2000;Akiba et al, 2002;Callison-Burch & Flournoy, 2001;Nomoto, 2004;Jayaraman & Lavie, 2005;Matusov et al, 2006;Rosti, et al, 2007;Chen et al, 2007). MEMT has the potential to achieve significantly better performance than any single MT system (Callison-Burch, et al, 2008).…”

Section: Translation Strategies For Metadata Recordsmentioning

confidence: 99%

A preliminary evaluation of metadata records machine translation

Chen

Ding²,

Jiang³

et al. 2012

The Electronic Library

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Purpose of this paperThe purpose of this study is to evaluate freely available machine translation (MT) services' performance in translating metadata records. Design/methodology/approachRandomly selected metadata records were translated from English into Chinese using Google, Bing, and SYSTRAN Machine Translation (MT) systems. These translations were then evaluated using a five point scale for both Fluency and Adequacy. Missing Count (words not translated) and Incorrect Count (words incorrectly translated) were also recorded. FindingsConcerning both Fluency and Adequacy, Google and Bing's translations of more than 70% of test data received scores equal to or greater than three, representative of 'non-native Chinese' and 'much coverage,' respectively. SYSTRAN scored lowest in both measures. However, these differences were not statistically significant. A Pearson correlation analysis demonstrated a strong relationship (r= .86) between Fluency and Adequacy. Missing Count and Incorrect Count strongly correlated with Fluency and Adequacy. Research limitations/implicationsThis study was conducted in a specific domain with a small sample size. It is necessary to conduct the evaluation with a larger, more representative test dataset. Also, other language pairs should be evaluated applying similar technologies. Originality/valueMost existing digital collections can be accessed in English alone. Few digital collections in the United States support multilingual information access (MLIA) that enables users of differing languages to search, browse, recognize and use information in the collections. Human translation is one solution, but it is neither time nor cost effective for most libraries. This study serves as a first step to understand the 2 performance of current MT systems and to design effective and efficient MLIA services for digital collections.

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Section: Translation Strategies For Metadata Recordsmentioning

confidence: 99%

A preliminary evaluation of metadata records machine translation

Chen

Ding²,

Jiang³

et al. 2012

The Electronic Library

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“…We instead integrate the translation models from multiple sources by extending the phrase table. In contrast to the prior approach presented in (Chen et al, 2007) and (Eisele et al, 2008) which concatenates the phrase tables and adds new features as system markers, our extension method avoids duplicate entries in the final combined table.…”

Section: Integration Into Existing Smt Systemmentioning

confidence: 99%

Combining multi-engine translations with Moses

Chen

Jellinghaus

Eisele

et al. 2009

Proceedings of the Fourth Workshop on Statistical Machine Translation - StatMT '09

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“…1 Introduction (Chen et al, 2007) describes an architecture that allows to combine statistical machine translation (SMT) with one or multiple rule-based machine translation (RBMT) systems in a multi-engine setup. It uses a variant of standard SMT technology to align translations from one or more RBMT systems with the source text and incorporated phrases extracted from these alignments into the phrase table of the SMT system.…”

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confidence: 99%