Machine translation between Hebrew and Arabic

Shilon, Reshef; Habash, Nizar; Lavie, Alon; Wintner, Shuly

doi:10.1007/s10590-011-9103-z

Cited by 8 publications

(6 citation statements)

References 17 publications

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“…The result showed that the pivot-based approach performed better than the direct translation. Although the pivoting strategy provides a solution to the lack of parallel data, it causes information loss when using a morphologically poor language, such as English, as a pivot language [19]. In [20], the authors collected a corpus from United Nations (UN) documentation and used it to build standard PBSMT systems of Arabic-Chinese and Chinese-Arabic translations.…”

Section: Related Workmentioning

confidence: 99%

Improved Arabic–Chinese Machine Translation with Linguistic Input Features

et al. 2019

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This study presents linguistically augmented models of phrase-based statistical machine translation (PBSMT) using different linguistic features (factors) on the top of the source surface form. The architecture addresses two major problems occurring in machine translation, namely the poor performance of direct translation from a highly-inflected and morphologically complex language into morphologically poor languages, and the data sparseness issue, which becomes a significant challenge under low-resource conditions. We use three factors (lemma, part-of-speech tags, and morphological features) to enrich the input side with additional information to improve the quality of direct translation from Arabic to Chinese, considering the importance and global presence of this language pair as well as the limitation of work on machine translation between these two languages. In an effort to deal with the issue of the out of vocabulary (OOV) words and missing words, we propose the best combination of factors and models based on alternative paths. The proposed models were compared with the standard PBSMT model which represents the baseline of this work, and two enhanced approaches tokenized by a state-of-the-art external tool that has been proven to be useful for Arabic as a morphologically rich and complex language. The experiment was performed with a Moses decoder on freely available data extracted from a multilingual corpus from United Nation documents (MultiUN). Results of a preliminary evaluation in terms of BLEU scores show that the use of linguistic features on the Arabic side considerably outperforms baseline and tokenized approaches, the system can consistently reduce the OOV rate as well.

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Section: Related Workmentioning

confidence: 99%

Improved Arabic–Chinese Machine Translation with Linguistic Input Features

et al. 2019

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“…For Arabic MT, the past 10 years have witnessed a lot of interest in translating from Arabic to English mostly due to large DARPA programs such as GALE and BOLT (Olive et al, 2011). There have been some limited efforts in comparison on translating into Arabic from English (Hamon and Choukri, 2011;Al-Haj and Lavie, 2012;El Kholy and Habash, 2012), but also between Arabic and other languages (Boudabous et al, 2013;Habash and Hu, 2009;Shilon et al, 2012;Cettolo et al, 2011). The JRC-Acquis collection, of which we translate a portion, is publicly available for research purposes and already exists in 22 languages (and others ongoing).…”

Section: Related Workmentioning

confidence: 99%

A Parallel Corpus for Evaluating Machine Translation between Arabic and European Languages

Habash¹,

Zalmout

Taji

et al. 2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 2

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We present Arab-Acquis, a large publicly available dataset for evaluating machine translation between 22 European languages and Arabic. Arab-Acquis consists of over 12,000 sentences from the JRCAcquis (Acquis Communautaire) corpus translated twice by professional translators, once from English and once from French, and totaling over 600,000 words. The corpus follows previous data splits in the literature for tuning, development, and testing. We describe the corpus and how it was created. We also present the first benchmarking results on translating to and from Arabic for 22 European languages.

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“…Most contemporary algorithms still use English as a pivot language even when translating between languages that resemble each other (e.g. Arabic and Hebrew) more than either one of them resembles English (Shilon et al, 2012; Wintner, 2004). Since Translate’s basic model relies on corpora of texts, languages with more varied texts translations to and from English (like French) are better represented in it.…”

Section: An Inverse Tower: From English To Interlinguamentioning

confidence: 99%

Uniform multilingualism: A media genealogy of Google Translate

Ramati

Pinchevski

2017

New Media & Society

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This article applies a media geneaology perspective to examine the operative logic of Google Translate. Tracing machine translation from post–World War II (WWII) rule-based methods to contemporary algorithmic statistical methods, we analyze the underlying power structure of algorithmic and human collaboration that Translate encompasses. Focusing on the relationship between technology, language, and speakers, we argue that the operative logic of Translate represents a new model of translation, which we call uniform multilingualism. In this model, the manifest lingual plurality on the user side is mediated by lingual uniformity on the system side in the form of an English language algorithm, which has recently given way to an artificial neural network interlingual algorithm. We conclude by considering the significance of this recent shift in Translate’s algorithm.

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Machine translation between Hebrew and Arabic

Cited by 8 publications

References 17 publications

Improved Arabic–Chinese Machine Translation with Linguistic Input Features

Improved Arabic–Chinese Machine Translation with Linguistic Input Features

A Parallel Corpus for Evaluating Machine Translation between Arabic and European Languages

Uniform multilingualism: A media genealogy of Google Translate

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