Clause-Complex Level Error Analysis of English–Chinese Machine Translation

2018 ◽  
pp. 563-572
Author(s):  
Shili Ge ◽  
Rou Yang ◽  
Dandan Ye ◽  
Xiaoxiao Chen ◽  
Rou Song
Keyword(s):  
Error Analysis ◽  
Machine Translation ◽  
Clause Complex

scholarly journals Towards Automatic Error Analysis of Machine Translation Output

2011 ◽  
Vol 37 (4) ◽  
pp. 657-688 ◽  
Author(s):  
Maja Popović ◽  
Hermann Ney
Keyword(s):  
Error Analysis ◽  
Machine Translation ◽  
Error Rate ◽  
Human Error ◽  
Translation System ◽  
Specific Information ◽  
Error Type ◽  
Word Error Rate ◽  
Advantages And Disadvantages ◽  
Automatic Error

Evaluation and error analysis of machine translation output are important but difficult tasks. In this article, we propose a framework for automatic error analysis and classification based on the identification of actual erroneous words using the algorithms for computation of Word Error Rate (WER) and Position-independent word Error Rate (PER), which is just a very first step towards development of automatic evaluation measures that provide more specific information of certain translation problems. The proposed approach enables the use of various types of linguistic knowledge in order to classify translation errors in many different ways. This work focuses on one possible set-up, namely, on five error categories: inflectional errors, errors due to wrong word order, missing words, extra words, and incorrect lexical choices. For each of the categories, we analyze the contribution of various POS classes. We compared the results of automatic error analysis with the results of human error analysis in order to investigate two possible applications: estimating the contribution of each error type in a given translation output in order to identify the main sources of errors for a given translation system, and comparing different translation outputs using the introduced error categories in order to obtain more information about advantages and disadvantages of different systems and possibilites for improvements, as well as about advantages and disadvantages of applied methods for improvements. We used Arabic–English Newswire and Broadcast News and Chinese–English Newswire outputs created in the framework of the GALE project, several Spanish and English European Parliament outputs generated during the TC-Star project, and three German–English outputs generated in the framework of the fourth Machine Translation Workshop. We show that our results correlate very well with the results of a human error analysis, and that all our metrics except the extra words reflect well the differences between different versions of the same translation system as well as the differences between different translation systems.

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

2016 ◽  
Vol 9 (3) ◽  
pp. 13 ◽  
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>

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

2011 ◽  
Vol 45 (2) ◽  
pp. 181-208 ◽  
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

scholarly journals Sentence-structure errors of machine translation into Slovak

2021 ◽  
Vol 22 (1) ◽  
pp. 78-92
Author(s):  
Katarína Welnitzová ◽  
Daša Munková
Keyword(s):  
Error Analysis ◽  
Machine Translation ◽  
Word Order ◽  
Academic Environment ◽  
Sentence Structure ◽  
Research Results ◽  
Interdisciplinary Character ◽  
Slovak Language

Abstract The study identifies, classifies and analyses errors in machine translation (MT) outputs of journalistic texts from English into Slovak, using error analysis. The research results presented in the study are pioneering, since the issue of machine translation – with its strong interdisciplinary character and novelty – has not yet been studied in the Slovak academic environment. The evaluation of the errors is based on a framework for classification of MT errors devised by Vaňko, which was arranged for the Slovak language. The study discusses and explains the issues of sentence structure, including predicativeness, syntactic-semantic correlativeness, and a modal and communication sentence framework. We discovered that the majority of the errors are related to the categories of agreement, word order and nominal morpho-syntax. This fact clearly correlates with features of journalistic texts, in which nominal structures and nouns in all realizations are used to a great extent. Moreover, there are some serious differences between the languages which limit and affect the quality of translation.

scholarly journals نظم الترجمة الآلية الإحصائية والتحويلية: دراسة تحليلية مقارنة (The statistical and transformative setups in machine translation: A comparative analysis)

1970 ◽  
Vol 3 (1) ◽  
Author(s):  
مجدي حاج إبراهيم (Majdi Haji Ibrahim) ◽  
عائشة رابع محمد (Aisha Rabi' Mohammed)
Keyword(s):  
Comparative Analysis ◽  
Error Analysis ◽  
Machine Translation ◽  
Analysis Method

ملخص البحث:يهدف هذا البحث -في إطاره النظري- إلى دراسة نظم الترجمة الآلية وأنواعها، ونهجها، وكيفية عملها. وفي الجانب التطبيقي يسعى البحث إلى عقد مقارنة بين نظم الترجمة الآلية الإحصائية والتحويلية، كما سيقوم البحث أيضاً بإجراء مقارنة بين ترجمة نظام (جوجل) الذي يعتمد على نظام الترجمة الآلية الإحصائية، ونظام (عجيب) الذي يعتمد على نظام الترجمة الآلية التحويلية؛ وذلك من أجل الوقوف على مدى قدرة نظم الترجمة الآلية المختلفة على ترجمة النصوص من اللغة الإنجليزية إلى اللغة العربية. وسيتبع البحث منهج تحليل الأخطاء في عملية إجراء المقارنة بين ترجمات النظامين المذكورين.الكلمات المفتاحية: الترجمة الآلية- تطبيقات- جوجل- عجيب- المقارنةAbstract:The paper aims – theoretically – to study the types of setup in machine translation, the methods used and their processes. In the application aspect, it tries to compare between the statistical and transformative setups in machine translation. Therefore, a comparison between the translation results of Google and ‘Ajeeb will be carried out accordingly; the former being a statistical setup and the latter a transformative. This will enable us to discover the degree of success of the two setups in translating setup English and Arabic. The study makes use of the error analysis method in comparing the results of both setups.Keywords: Machine translation – applications – google - comparisonAbstrak:Kajian ini dari segi teorinya membahaskan sistem penterjemahan automatik, jenis, prosedur serta fungsinya. Dari segi praktikal pula, kajian ini akan membandingkan antara penterjemahan komputatif dan transformatif dan turut membuat perbandingan antara program penterjemahan Google yang mengaplikasikan sistem penterjemahan automatik komputatif serta program penterjemahan ‘Ajib Google yang mengaplikasikan sistem penterjemahan automatik transformatif. Ini adalah bertujuan menentukan kadar perbezaan antara kedua program dalam menterjemahkan teks berbahasa Inggeris ke bahasa Arab. Kajian ini akan menggunakan metodologi ‘analisa ralat’ dalam proses perbandingan tersebut.Kata kunci: Penterjemahan Automatik – aplikasi – Google – ‘Ajib – perbandingan 

Statistical Error Analysis of Machine Translation: The Case of Arabic

2020 ◽  
Vol 24 (3) ◽  
Author(s):  
Mohamed El Marouani ◽  
Tarik Boudaa ◽  
Nourddine Enneya
Keyword(s):  
Error Analysis ◽  
Machine Translation ◽  
Statistical Error
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