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Transliteration

Transliteration is a mechanism for converting a word in a source (foreign) language to a target language, and often adopts approaches from machine translation. In machine translation, the objective is to preserve the semantic meaning of the utterance as much as possible while following the syntactic structure in the target language. In Transliteration, the objective is to preserve the original pronunciation of the source word as much as possible while following the phonological structures of the target language.

For example, the city’s name “Manchester” has become well known by people of languages other than English. These new words are often named entities that are important in cross-lingual information retrieval, information extraction, machine translation, and often present out-of-vocabulary challenges to spoken language technologies such as automatic speech recognition, spoken keyword search, and text-to-speech.

Source: Phonology-Augmented Statistical Framework for Machine Transliteration using Limited Linguistic Resources

Papers

Showing 271280 of 435 papers

TitleStatusHype
Translation of Unseen Bigrams by Analogy Using an SVM Classifier0
amLite: Amharic Transliteration Using Key Map Dictionary0
Do we need bigram alignment models? On the effect of alignment quality on transduction accuracy in G2P0
Training Automatic Transliteration Models on DBPedia Data0
Improving Arabic Diacritization through Syntactic Analysis0
Semi-supervised Chinese Word Segmentation based on Bilingual Information0
Improving Statistical Machine Translation with a Multilingual Paraphrase Database0
Arabic Diacritization with Recurrent Neural Networks0
Data representation methods and use of mined corpora for Indian language transliteration0
Boosting English-Chinese Machine Transliteration via High Quality Alignment and Multilingual Resources0
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