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Transliteration with Other Methods

A number of different machine learning methods have been applied to the transliteration task.

Transliteration With Other Methods is the main subject of 31 publications.

Topics in Transliteration

Transliteration With FSM | Transliteration With Other Methods | Forward Transliteration | Transliteration Training Data | Integrating Transliteration


Improvements have been shown when including context information in a maximum entropy model (Goto et al., 2003; Goto et al., 2004). Other machine learning methods such as discriminative training with the perceptron algorithm have been applied to transliteration (Zelenko and Aone, 2006). Oh and Choi (2002) use a pipeline of different approaches for the various mapping stages. Lin and Chen (2002) present a gradient descent method to map phoneme sequences. Using phoneme and letter chunks may lead to better performance (Kang and Kim, 2000). Li et al. (2004) propose a method similar to the joint model for phrase-based machine translation. Such a substring mapping model has also been explored by others (Ekbal et al., 2006), and may be implemented as bi-stream HMM (Zhao et al., 2007) or adapting a monotone phrase-based translation model (Sherif and Kondrak, 2007). For different scripts for the same language, rule-based approaches typically suffice (Malik, 2006). Ensemble learning, i.e., the combination of multiple transliteration engines, has been shown to be successful (Oh and Isahara, 2007).



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  • Finch et al. (2015)
  • Ammar et al. (2012)
  • Hagiwara and Sekine (2012)
  • Kuo et al. (2012)
  • Bhargava and Kondrak (2011)
  • Hagiwara and Sekine (2011)
  • Ravi and Knight (2009)
  • Cherry and Suzuki (2009)
  • Li et al. (2009)
  • Li et al. (2009)
  • Durrani et al. (2010)
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  • Kirschenbaum and Wintner (2009)
  • Li and Yarowsky (2008)
  • Aramaki et al. (2008)
  • Kuo and Li (2008)
  • Surana and Singh (2008)
  • Liao (2008)
  • Finch and Sumita (2008)