Automatic Transliteration of Romanized Dialectal Arabic

Abstract

In this paper, we address the problem of converting Dialectal Arabic (DA) text that is written in the Latin script (called Arabizi) into Arabic script following the CODA convention for DA orthography. The presented system uses a finite state transducer trained at the character level to generate all possible transliterations for the input Arabizi words. We then filter the generated list using a DA morphological analyzer. After that we pick the best choice for each input word using a language model. We achieve an accuracy of 69.4% on an unseen test set compared to 63.1% using a system which represents a previously proposed approach.

Arabic Transliteration of Romanized Tunisian Dialect Text: A Preliminary Investigation

“…It replaces a sound with a Latin letter or a group of Latin letters. This mainly depends on language-specific assumptions about grapheme-to-phoneme mapping [1]. The same is true for the Tunisian Dialect, which has no standard Arabic-script orthography.…”

“…We noticed that a Dialect word can be written in several ways because in cases where there is no standard orthography, people use a spontaneous orthography that is based on various criteria [1]. The main criterion is phonology.…”

“…Second, for Arabic words, he modeled transliteration from Arabizi to Arabic script, and then applied language modeling on the transliterated text. Finally, [1] presented a system that converts a DA (Egyptian Arabic) text that is written with Latin script (called Arabizi) into Arabic script following the CODA convention for DA orthography. This system uses a finite state transducer trained at the character level to generate all possible transliterations for the input of Arabizi words.…”

Robust Dictionary Lookup in Multiple Noisy Orthographies

“…El-Kahky et al (2011) use graph reinforcement models to learn mapping between characters in different scripts in the context of transliteration mining. Al-Badrashiny et al (2014) present a similar system where words are transcribed using a finite state transducer constructed from an aligned parallel Arabizi-Arabic corpus. The disadvantage of this and other learned methods (Ristad and Yianilos, 1998;Lin and Chen, 2002;Mangu and Brill, 1997;Jiampojamarn et al, 2009) is that they require aligned parallel corpora whereas our approach performs well without any data.…”

“…Transliteration Transliteration systems invariably use either a direct mapping between substrings in the two scripts (Al-Onaizan and Knight, 2002;AbdulJaleel and Larkey, 2003;El-Kahky et al, 2011;Al-Badrashiny et al, 2014), or use an intermediate script such as the International Phonetic Alphabet (IPA) (Brawer et al, 2010) or Double Metaphones (Philips, 2000). AbdulJaleel and Larkey (2003) proposed a statistical method for transliteration between Arabic and English.…”