Czech Machine Translation in the project CzechMate

Abstract We present various achievements in statistical machine translation from English, German, Spanish and French into Czech. We discuss specific properties of the individual source languages and describe techniques that exploit these properties and address language-specific errors. Besides the translation proper, we also present our contribution to error analysis.

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A Comparative Study of Google Translate Translations: An Error Analysis of English-to-Persian and Persian-to-English Translations

English Language Teaching ◽

10.5539/elt.v9n3p13 ◽

2016 ◽

Vol 9 (3) ◽

pp. 13 ◽

Cited By ~ 1

Author(s):

Hadis Ghasemi ◽

Mahmood Hashemian

Keyword(s):

Error Analysis ◽

Machine Translation ◽

English Translation ◽

Statistical Machine Translation ◽

Future Research ◽

Comparison Study ◽

Chi Square ◽

Passive Voice ◽

English Translations ◽

Chi Square Test

<p>Both lack of time and the need to translate texts for numerous reasons brought about an increase in studying machine translation with a history spanning over 65 years. During the last decades, Google Translate, as a statistical machine translation (SMT), was in the center of attention for supporting 90 languages. Although there are many studies on Google Translate, few researchers have considered Persian-English translation pairs. This study used Keshavarzʼs (1999) model of error analysis to carry out a comparison study between the raw English-Persian translations and Persian-English translations from Google Translate. Based on the criteria presented in the model, 100 systematically selected sentences from an interpreter app called Motarjem Hamrah were translated by Google Translate and then evaluated and brought in different tables. Results of analyzing and tabulating the frequencies of the errors together with conducting a chi-square test showed no significant differences between the qualities of Google Translate from English to Persian and Persian to English. In addition, lexicosemantic and active/passive voice errors were the most and least frequent errors, respectively. Directions for future research are recognized in the paper for the improvements of the system.</p>

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Overcoming statistical machine translation limitations: error analysis and proposed solutions for the Catalan–Spanish language pair

Language Resources and Evaluation ◽

10.1007/s10579-011-9137-0 ◽

2011 ◽

Vol 45 (2) ◽

pp. 181-208 ◽

Cited By ~ 8

Author(s):

Mireia Farrús ◽

Marta R. Costa-jussà ◽

José B. Mariño ◽

Marc Poch ◽

Adolfo Hernández ◽

...

Keyword(s):

Error Analysis ◽

Machine Translation ◽

Statistical Machine Translation ◽

Spanish Language ◽

Language Pair

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Quiz-Based Evaluation of Machine Translation

Prague Bulletin of Mathematical Linguistics ◽

10.2478/v10108-011-0006-1 ◽

2011 ◽

Vol 95 (1) ◽

pp. 77-86 ◽

Cited By ~ 2

Author(s):

Jan Berka ◽

Martin Černý ◽

Ondřej Bojar

Keyword(s):

Machine Translation ◽

Statistical Machine Translation ◽

New Method ◽

The Individual ◽

Extract Information

Quiz-Based Evaluation of Machine Translation This paper proposes a new method of manual evaluation for statistical machine translation, the so-called quiz-based evaluation, estimating whether people are able to extract information from machine-translated texts reliably. We apply the method to two commercial and two experimental MT systems that participated in WMT 2010 in English-to-Czech translation. We report inter-annotator agreement for the evaluation as well as the outcomes of the individual systems. The quiz-based evaluation suggests rather different ranking of the systems compared to the WMT 2010 manual and automatic metrics. We also see that overall, MT quality is becoming acceptable for obtaining information from the text: about 80% of questions can be answered correctly given only machine-translated text.

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Developing Statistical Machine Translation System for English and Nigerian Languages

Asian Journal of Research in Computer Science ◽

10.9734/ajrcos/2018/v1i424761 ◽

2018 ◽

pp. 1-8

Author(s):

Ignatius Ikechukwu Ayogu ◽

Adebayo Olusola Adetunmbi ◽

Bolanle Adefowoke Ojokoh

Keyword(s):

Error Analysis ◽

Machine Translation ◽

Statistical Machine Translation ◽

Translation System ◽

Parallel Corpora ◽

Translation Tools ◽

Global Demand ◽

Machine Translation System ◽

Translation Systems

The global demand for translation and translation tools currently surpasses the capacity of available solutions. Besides, there is no one-solution-fits-all, off-the-shelf solution for all languages. Thus, the need and urgency to increase the scale of research for the development of translation tools and devices continue to grow, especially for languages suffering under the pressure of globalisation. This paper discusses our experiments on translation systems between English and two Nigerian languages: Igbo and Yorùbá. The study is setup to build parallel corpora, train and experiment English-to-Igbo, (), English-to-Yorùbá, () and Igbo-to-Yorùbá, () phrase-based statistical machine translation systems. The systems were trained on parallel corpora that were created for each language pair using text from the religious domain in the course of this research. A BLEU score of 30.04, 29.01 and 18.72 respectively was recorded for the English-to-Igbo, English-to-Yorùbá and Igbo-to-Yorùbá MT systems. An error analysis of the systems’ outputs was conducted using a linguistically motivated MT error analysis approach and it showed that errors occurred mostly at the lexical, grammatical and semantic levels. While the study reveals the potentials of our corpora, it also shows that the size of the corpora is yet an issue that requires further attention. Thus an important target in the immediate future is to increase the quantity and quality of the data.

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Morpho-syntactic information for automatic error analysis of statistical machine translation output

10.3115/1654650.1654652 ◽

2006 ◽

Cited By ~ 6

Author(s):

Maja Popović ◽

Hermann Ney ◽

Adrià de Gispert ◽

José B. Mariño ◽

Deepa Gupta ◽

...

Keyword(s):

Error Analysis ◽

Machine Translation ◽

Statistical Machine Translation ◽

Syntactic Information ◽

Automatic Error

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Factored Statistical Machine Translation for German-English

Journal of Applied Information, Communication and Technology ◽

10.33555/ejaict.v5i1.47 ◽

2018 ◽

Vol 5 (1) ◽

pp. 37-45

Author(s):

Darryl Yunus Sulistyan

Keyword(s):

Machine Translation ◽

English Language ◽

Statistical Machine Translation ◽

New Model ◽

Language Pair

Machine Translation is a machine that is going to automatically translate given sentences in a language to other particular language. This paper aims to test the effectiveness of a new model of machine translation which is factored machine translation. We compare the performance of the unfactored system as our baseline compared to the factored model in terms of BLEU score. We test the model in German-English language pair using Europarl corpus. The tools we are using is called MOSES. It is freely downloadable and use. We found, however, that the unfactored model scored over 24 in BLEU and outperforms the factored model which scored below 24 in BLEU for all cases. In terms of words being translated, however, all of factored models outperforms the unfactored model.

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