An Efficient Method to Add Chunker Rules in Persian to English Rule-based Apertium Machine Translation System


  • Pariya Razmdideh Vali-e-Asr University of Rafsanjan
  • Abbas Ali Ahangar University of Sistan and Baluchestan
  • Seyed Mojtaba Sabbagh-Jafari Vali-e-Asr University of Rafsanjan
  • Gholamreza Haffari Monash University


Pool-based active learning, Rule-based machine translation, Apertium, Chunker rules


Rule-based machine translation (RBMT) captures linguistic information about the source and target languages. This information is retrieved from (bilingual) dictionaries and grammar rules. This paper proposes an active learning (AL) method to grow structural transfer rules at the chunker level. To this end, two sets of experiments are performed based on two types of sentences extracted from Mizan English-Persian Parallel Corpus which are selected manually and randomly. The results show adding newly written chunker rules to the transformation file using pool-based AL technique improves translation system more compared to a random chunker rule selection baseline.

Author Biographies

Pariya Razmdideh, Vali-e-Asr University of Rafsanjan

Assistant Professor of Linguistics, Vali-e-Asr University of Rafsanjan, Iran;

Abbas Ali Ahangar, University of Sistan and Baluchestan

Associate Professor of Linguistics, University of Sistan and Baluchestan, Iran;

Seyed Mojtaba Sabbagh-Jafari, Vali-e-Asr University of Rafsanjan

Assistant Professor of Computer Engineering, Vali-e-Asr University of Rafsanjan, Iran;

Gholamreza Haffari, Monash University

Associate Professor at Faculty of Information Technology, Monash University, Australia;


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How to Cite

Razmdideh, P., Ahangar, A. A., Sabbagh-Jafari, S. M., & Haffari, G. (2019). An Efficient Method to Add Chunker Rules in Persian to English Rule-based Apertium Machine Translation System. Translation Studies Quarterly, 17(65), 54–73. Retrieved from



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