Multi-engine machine translation with voted language model

Nomoto, Tadashi

doi:10.3115/1218955.1219018

Cited by 15 publications

(16 citation statements)

References 14 publications

(13 reference statements)

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“…In the competitive combination mode (b), the three translation engines separately translate the input sentence or the components of the input sentence, and the system then selects the best of the three results as the output (Frederking, and Nirenburg 1994;Nomoto 2003Nomoto , 2004. Several studies have shown that the overall performance of the competitive arrangement can in fact, as hoped, be better than that of the best participating MT engine (Hogan and Frederking 1998;Cavar, et al 2000;Akiba et al 2002).…”

Section: Mt and The Usermentioning

confidence: 99%

Sound effects in translation

Mees

Dragsted²,

Hansen³

et al. 2013

Target

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This paper argues that the time is now right to field practical Spoken Language Translation (SLT) systems. Several sorts of practical systems can be built over the next few years if system builders recognize that, at the present state of the art, users must cooperate and compromise with the programs. Further, SLT systems can be arranged on a scale, in terms of the degree of cooperation or compromise they require from users. In general, the broader the intended linguistic or topical coverage of a system, the more user cooperation or compromise it will presently require. The paper briefly discusses the component technologies of SLT systems as they relate to user cooperation and accommodation ("human factors engineering"), with examples from the authors' work. It describes three classes of "cooperative" SLT systems which could be put into practical use during the next few years.

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Section: Mt and The Usermentioning

confidence: 99%

Sound effects in translation

Mees

Dragsted²,

Hansen³

et al. 2013

Target

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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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“…Sentence-level combination is done by choosing one hypothesis among multiple MT system outputs (and possibly among n-best lists). The selection criterion can be a combination of translation model and language model scores with multiple comparison tests (Akiba et al, 2002), or statistical confidence models (Nomoto, 2004).…”

Section: Related Workmentioning

confidence: 99%

Improving alignments for better confusion networks for combining machine translation systems

Ayan

Zheng

Wang

2008

Proceedings of the 22nd International Conference on Computational Linguistics - COLING '08

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The state-of-the-art system combination method for machine translation (MT) is the word-based combination using confusion networks. One of the crucial steps in confusion network decoding is the alignment of different hypotheses to each other when building a network. In this paper, we present new methods to improve alignment of hypotheses using word synonyms and a two-pass alignment strategy. We demonstrate that combination with the new alignment technique yields up to 2.9 BLEU point improvement over the best input system and up to 1.3 BLEU point improvement over a state-of-the-art combination method on two different language pairs.

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Multi-engine machine translation with voted language model

Cited by 15 publications

References 14 publications

Sound effects in translation

Sound effects in translation

A preliminary evaluation of metadata records machine translation

Improving alignments for better confusion networks for combining machine translation systems

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