scholarly journals Quiz-Based Evaluation of Machine Translation

2011 ◽  
Vol 95 (1) ◽  
pp. 77-86 ◽  
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.

scholarly journals Czech Machine Translation in the project CzechMate

2014 ◽  
Vol 101 (1) ◽  
pp. 71-96 ◽  
Author(s):  
Ondřej Bojar ◽  
Daniel Zeman
Keyword(s):  
Error Analysis ◽  
Machine Translation ◽  
Statistical Machine Translation ◽  
Individual Source ◽  
The Individual

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.

Factored Statistical Machine Translation for German-English

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.

Synchronous Tree Sequence Substitution Grammar for Statistical Machine Translation

2009 ◽  
Vol 35 (10) ◽  
pp. 1317-1326
Author(s):  
Hong-Fei JIANG ◽  
Sheng LI ◽  
Min ZHANG ◽  
Tie-Jun ZHAO ◽  
Mu-Yun YANG
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Sequence Substitution

Analysis Accuracy of Similar Word Based Clustering (EWSB) Algorithm on Machine Translator Bahasa Indonesia-Minang

2018 ◽  
Vol 3 (3) ◽  
Author(s):  
Herry Sujaini
Keyword(s):  
Machine Translation ◽  
Clustering Algorithm ◽  
Statistical Machine Translation ◽  
Target Language ◽  
Word Similarity ◽  
Similar Word ◽  
Word Clustering ◽  
Translation Accuracy ◽  
Bahasa Indonesia

Extended Word Similarity Based (EWSB) Clustering is a word clustering algorithm based on the value of words similarity obtained from the computation of a corpus. One of the benefits of clustering with this algorithm is to improve the translation of a statistical machine translation. Previous research proved that EWSB algorithm could improve the Indonesian-English translator, where the algorithm was applied to Indonesian language as target language.This paper discusses the results of a research using EWSB algorithm on a Indonesian to Minang statistical machine translator, where the algorithm is applied to Minang language as the target language. The research obtained resulted that the EWSB algorithm is quite effective when used in Minang language as the target language. The results of this study indicate that EWSB algorithm can improve the translation accuracy by 6.36%.

English-Dogri Translation System using MOSES

2016 ◽  
Vol 1 (1) ◽  
pp. 45-49
Author(s):  
Avinash Singh ◽  
Asmeet Kour ◽  
Shubhnandan S. Jamwal
Keyword(s):  
Natural Language Processing ◽  
Machine Translation ◽  
Language Processing ◽  
Statistical Machine Translation ◽  
Translation System ◽  
Parallel Corpus ◽  
English System ◽  
Machine Translation System ◽  
Translation Machine ◽  
Language Pair

The objective behind this paper is to analyze the English-Dogri parallel corpus translation. Machine translation is the translation from one language into another language. Machine translation is the biggest application of the Natural Language Processing (NLP). Moses is statistical machine translation system allow to train translation models for any language pair. We have developed translation system using Statistical based approach which helps in translating English to Dogri and vice versa. The parallel corpus consists of 98,973 sentences. The system gives accuracy of 80% in translating English to Dogri and the system gives accuracy of 87% in translating Dogri to English system.

scholarly journals Design of Personalized Devices—The Tradeoff between Individual Value and Personalization Workload

2020 ◽  
Vol 11 (1) ◽  
pp. 241
Author(s):  
Juliane Kuhl ◽  
Andreas Ding ◽  
Ngoc Tuan Ngo ◽  
Andres Braschkat ◽  
Jens Fiehler ◽  
...  
Keyword(s):  
Customer Value ◽  
Early Stage ◽  
Treatment Success ◽  
Product Family ◽  
Flow Diverter ◽  
New Method ◽  
Product Families ◽  
Wide Range ◽  
The Individual ◽  
Individual Value

Personalized medical devices adapted to the anatomy of the individual promise greater treatment success for patients, thus increasing the individual value of the product. In order to cater to individual adaptations, however, medical device companies need to be able to handle a wide range of internal processes and components. These are here referred to collectively as the personalization workload. Consequently, support is required in order to evaluate how best to target product personalization. Since the approaches presented in the literature are not able to sufficiently meet this demand, this paper introduces a new method that can be used to define an appropriate variety level for a product family taking into account standardized, variant, and personalized attributes. The new method enables the identification and evaluation of personalizable attributes within an existing product family. The method is based on established steps and tools from the field of variant-oriented product design, and is applied using a flow diverter—an implant for the treatment of aneurysm diseases—as an example product. The personalization relevance and adaptation workload for the product characteristics that constitute the differentiating product properties were analyzed and compared in order to determine a tradeoff between customer value and personalization workload. This will consequently help companies to employ targeted, deliberate personalization when designing their product families by enabling them to factor variety-induced complexity and customer value into their thinking at an early stage, thus allowing them to critically evaluate a personalization project.

Reordering space design in statistical machine translation

2016 ◽  
Vol 50 (2) ◽  
pp. 375-410
Author(s):  
Nicolas Pécheux ◽  
Alexandre Allauzen ◽  
Jan Niehues ◽  
François Yvon
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Space Design
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