Arabic Machine Transliteration using an Attention-based Encoder-decoder Model

Abstract Transliteration is the process of converting words from a given source language alphabet to a target language alphabet, in a way that best preserves the phonetic and orthographic aspects of the transliterated words. Even though an important effort has been made towards improving this process for many languages such as English, French and Chinese, little research work has been accomplished with regard to the Arabic language. In this work, an attention-based encoder-decoder system is proposed for the task of Machine Transliteration between the Arabic and English languages. Our experiments proved the efficiency of our proposal approach in comparison to some previous research developed in this area.

[1]  Mansur Arbabi,et al.  Algorithms for Arabic name transliteration , 1994, IBM J. Res. Dev..

[2]  Tetsuya Ishikawa,et al.  Japanese/English Cross-Language Information Retrieval: Exploration of Query Translation and Transliteration , 2001, Comput. Humanit..

[3]  Yoshua Bengio,et al.  On the Properties of Neural Machine Translation: Encoder–Decoder Approaches , 2014, SSST@EMNLP.

[4]  Hermann Ney,et al.  Phrase-Based Statistical Machine Translation , 2002, KI.

[5]  Steven Skiena,et al.  False-Friend Detection and Entity Matching via Unsupervised Transliteration , 2016, ArXiv.

[6]  Sanjeev Khudanpur,et al.  Transliteration of Proper Names in Cross-Lingual Information Retrieval , 2003, NER@ACL.

[7]  Hermann Ney,et al.  Discriminative Training and Maximum Entropy Models for Statistical Machine Translation , 2002, ACL.

[8]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[9]  Christopher D. Manning,et al.  Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling , 2005, ACL.

[10]  Nizar Habash,et al.  Four Techniques for Online Handling of Out-of-Vocabulary Words in Arabic-English Statistical Machine Translation , 2008, ACL.

[11]  Long Jiang,et al.  Named Entity Translation with Web Mining and Transliteration , 2007, IJCAI.

[12]  Ronald J. Williams,et al.  A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.

[13]  John Cocke,et al.  A Statistical Approach to Machine Translation , 1990, CL.

[14]  Hermann Ney,et al.  A Deep Learning Approach to Machine Transliteration , 2009, WMT@EACL.

[15]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[16]  Franz Josef Och,et al.  Minimum Error Rate Training in Statistical Machine Translation , 2003, ACL.

[17]  Philipp Koehn,et al.  Moses: Open Source Toolkit for Statistical Machine Translation , 2007, ACL.

[18]  Miles Osborne,et al.  Statistical Machine Translation , 2010, Encyclopedia of Machine Learning and Data Mining.

[19]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[20]  Kuldip K. Paliwal,et al.  Bidirectional recurrent neural networks , 1997, IEEE Trans. Signal Process..

[21]  Lemao Liu,et al.  Target-Bidirectional Neural Models for Machine Transliteration , 2016, NEWS@ACM.

[22]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[23]  Kevin Knight,et al.  Name Translation in Statistical Machine Translation - Learning When to Transliterate , 2008, ACL.

[24]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[25]  Christoph Goller,et al.  Learning task-dependent distributed representations by backpropagation through structure , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

[26]  Joakim Nivre,et al.  Applying Neural Networks to English-Chinese Named Entity Transliteration , 2016, NEWS@ACM.