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A Novel Approach by Injecting CCG Supertags into an Arabic-English Factored Translation Machine.

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Title: A Novel Approach by Injecting CCG Supertags into an Arabic-English Factored Translation Machine.
Authors: Rajeh, Hamdi1 hamdiahmed919@gmail.com, Li, Zhiyong1 zhiyong.li@hnu.edu.cn, Ayedh, Abdullah2 aiuth@yahoo.com
Source: Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ). Aug2016, Vol. 41 Issue 8, p3071-3080. 10p.
Subjects: Combinatory logic, Grammar, Computer software
Abstract: This study addresses the integration and incorporation of rich additional information into the phrase-based approach, aptly called factored translation, which is an extension of phrase-based statistic machine translation (PBSMT). This approach was proven successful when translating English into a morphologically rich language. PBSMT represents the baseline of this work. We extend the phrase-based translation approach by integrating additional linguistic knowledge, namely part-of-speech (POS) tags, to create a factored model. The main contribution of this study is the creation of a new approach for Arabic-English translation via the injection of the factored model into Combinatory Categorial Grammar (CCG) supertags to form an integrated model (POS + CCG). The system was trained on a freely available multi-UN corpus on Arabic-English language pairs. Moses decoder, which is an open-source factored SMT system, was used to integrate these data into the target language model and the target side of the translation model. Results showed improvements to the BLEU automatic score via various high n-gram language models (LMs). The integration of the featured factors (POS + CCG) of the translation has been successfully tested. Overall, the 3-, 5-, 7-, and 9-g LM evaluation with BLEU scores proved that our integrated model performed better than PBSMT. Compared with three other models (PBSMT, POS, and CCG models), the integrated model improved the translation quality by 1.54, 1.29, and 0.21 %, respectively, over the 3-g LM. [ABSTRACT FROM AUTHOR]
Copyright of Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: This study addresses the integration and incorporation of rich additional information into the phrase-based approach, aptly called factored translation, which is an extension of phrase-based statistic machine translation (PBSMT). This approach was proven successful when translating English into a morphologically rich language. PBSMT represents the baseline of this work. We extend the phrase-based translation approach by integrating additional linguistic knowledge, namely part-of-speech (POS) tags, to create a factored model. The main contribution of this study is the creation of a new approach for Arabic-English translation via the injection of the factored model into Combinatory Categorial Grammar (CCG) supertags to form an integrated model (POS + CCG). The system was trained on a freely available multi-UN corpus on Arabic-English language pairs. Moses decoder, which is an open-source factored SMT system, was used to integrate these data into the target language model and the target side of the translation model. Results showed improvements to the BLEU automatic score via various high n-gram language models (LMs). The integration of the featured factors (POS + CCG) of the translation has been successfully tested. Overall, the 3-, 5-, 7-, and 9-g LM evaluation with BLEU scores proved that our integrated model performed better than PBSMT. Compared with three other models (PBSMT, POS, and CCG models), the integrated model improved the translation quality by 1.54, 1.29, and 0.21 %, respectively, over the 3-g LM. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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