A Deep Learning Based Approach to Transliteration

In this paper, we propose different architectures for language independent machine transliteration which is extremely important for natural language processing (NLP) applications. Though a number of statistical models for transliteration have already been proposed in the past few decades, we proposed some neural network based deep learning architectures for the transliteration of named entities. Our transliteration systems adapt two different neural machine translation (NMT) frameworks: recurrent neural network and convolutional sequence to sequence based NMT. It is shown that our method provides quite satisfactory results when it comes to multi lingual machine transliteration. Our submitted runs are an ensemble of different transliteration systems for all the language pairs. In the NEWS 2018 Shared Task on Transliteration, our method achieves top performance for the En–Pe and Pe–En language pairs and comparable results for other cases.

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

[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.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

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

[5]  Rico Sennrich,et al.  Neural Machine Translation of Rare Words with Subword Units , 2015, ACL.

[6]  Binh Minh Nguyen,et al.  Regulating Orthography-Phonology Relationship for English to Thai Transliteration , 2016, NEWS@ACM.

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

[8]  Karthik Gali,et al.  Modeling Machine Transliteration as a Phrase Based Statistical Machine Translation Problem , 2009, NEWS@IJCNLP.

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

[10]  Kevin Knight,et al.  Machine Transliteration , 1997, CL.

[11]  Haizhou Li,et al.  Whitepaper of NEWS 2016 Shared Task on Machine Transliteration , 2016, NEWS@ACM.

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

[13]  Philip Koehn,et al.  Statistical Machine Translation , 2010, EAMT.

[14]  Thomas Breuel,et al.  Sequence-to-sequence neural network models for transliteration , 2016, ArXiv.

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

[16]  Yann Dauphin,et al.  Convolutional Sequence to Sequence Learning , 2017, ICML.

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

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