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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 251260 of 435 papers

TitleStatusHype
OFFLangOne@DravidianLangTech-EACL2021: Transformers with the Class Balanced Loss for Offensive Language Identification in Dravidian Code-Mixed text.0
Opinion Mining in a Code-Mixed Environment: A Case Study with Government Portals0
Optimizing Multilingual Text-To-Speech with Accents & Emotions0
Optimizing Transliteration for Hindi/Marathi to English Using only Two Weights0
Orthographic and Morphological Processing for Persian-to-English Statistical Machine Translation0
Palmyra: A Platform Independent Dependency Annotation Tool for Morphologically Rich Languages0
Part-of-Speech Tagging for Code-Switched, Transliterated Texts without Explicit Language Identification0
Phonologically Aware Neural Model for Named Entity Recognition in Low Resource Transfer Settings0
Phonology-Augmented Statistical Framework for Machine Transliteration using Limited Linguistic Resources0
PJAIT Systems for the IWSLT 2015 Evaluation Campaign Enhanced by Comparable Corpora0
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