Using language and translation models to select the best among outputs from multiple MT systems

Akiba, Yasuhiro; Watanabe, Takahiro; Sumita, Eiichiro

doi:10.3115/1072228.1072304

Cited by 26 publications

(36 citation statements)

References 6 publications

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“…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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“…This approach does not take into account the transition between fragments. Statistical approaches (Kaki et al 1999;Callison-Burch and Flournoy 2001;Akiba et al 2002;Imamura et al 2004) select translation fragments with a statistical model. The statistical model can solve the transition problem by using n-gram co-occurrence statistics.…”

Section: Translation Generationmentioning

confidence: 99%

Example-based machine translation based on tree–string correspondence and statistical generation

Liu¹,

Wang²,

Wu³

2006

Machine Translation

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Abstract. This paper describes an example-based machine translation (EBMT) method based on tree-string correspondence (TSC) and statistical generation. In this method, the translation example is represented as a TSC, which is a triple consisting of a parse tree in the source language, a string in the target language, and the correspondence between the leaf node of the source-language tree and the substring of the target-language string. For an input sentence to be translated, it is first parsed into a tree. Then the TSC forest which best matches the input tree is searched for. Finally the translation is generated using a statistical generation model to combine the target-language strings of the TSCs. The generation model consists of three features: the semantic similarity between the tree in the TSC and the input tree, the translation probability of translating the source word into the target word, and the language-model probability for the target-language string. Based on the above method, we build an English-to-Chinese MT system. Experimental results indicate that the performance of our system is comparable with phrase-based statistical MT systems.

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Using language and translation models to select the best among outputs from multiple MT systems

Cited by 26 publications

References 6 publications

Sound effects in translation

Sound effects in translation

A preliminary evaluation of metadata records machine translation

Example-based machine translation based on tree–string correspondence and statistical generation

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