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

Rajeh, Hamdi; Li, Zhiyong; Ayedh, Abdullah Mohammed

doi:10.1007/s13369-016-2075-9

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…For instance, the authors in [28] used additional features to create English-Arabic factored models and compare the performance with segmented models, the factored models achieved better quality. In [29], the authors developed a factored approach that improved Arabic-English translation by injecting POS and combinatory categorial grammar (CCG) into English, where factored models gained better quality enhanced the grammatical performance. The authors in [30] presented the results of PBSMT and a factored translation model between Korean and Chinese, in which the factored model performed a better MT quality compared to the standard PBSMT model.…”

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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“…Almaghout et al [5] combined the supertags for significant improvements for the Arabic-English and Chinese-English translation over the baseline models and gained progresses by presenting a system for labeling nonterminal in Hierarchical PBSMT. In addition, [6] improved the factored models by integrating CCG supertags into the target side of the Arabic to English PBSMT model. Our work differs with it in that our work dealing with source language by segmenting the Arabic word in the pre-processing step and integrating CCG supertags into the English target side as a factor; whereas in their case, the implementation was performed and lacked any segmentation.…”

Section: Related Workmentioning

confidence: 99%

The Impact of Word Segmentation on CCG-based Arabic-English SMT

Rajeh¹,

Li²,

Alghaili³

2017

dtcse

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Abstract. This paper presents a comparative study of two approaches to statistical machine translation (SMT). We present a study on Factored Machine Translation for the Arabic-English pair of languages. We illustrate pre-processing step for the Arabic source language and the new factors which added to the English target language. Our experiments that injected English side by part-ofspeech (POS) tags, Combinatory Categorical Grammar (CCG) supertags and segmented Arabic sentences displayed a considerable progress in terms of the BLEU scores. First experiments gained the premier examine of the Baseline phrase-based models during two approaches, Segmented (#S1) and non-segmented (#S2). In both approaches, CCG models acquired the greatest BLEU scores. The results show that the segmented approach which consists of CCG produce the highest accuracy at 30.91% (1.84% superior than baseline). As shown, the results presented considerable improvement in translating the segmented Arabic rather than the non-segmented into English language.

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Machine English Translation Scoring System Based on Deep Neural Network in the Internet of Things Environment

Xiao¹

2022

Cyber Security Intelligence and Analytics

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

Cited by 4 publications

References 16 publications

Improved Arabic–Chinese Machine Translation with Linguistic Input Features

Improved Arabic–Chinese Machine Translation with Linguistic Input Features

The Impact of Word Segmentation on CCG-based Arabic-English SMT

Machine English Translation Scoring System Based on Deep Neural Network in the Internet of Things Environment

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