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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 226250 of 435 papers

TitleStatusHype
How Grammatical is Character-level Neural Machine Translation? Assessing MT Quality with Contrastive Translation PairsCode0
Opinion Mining in a Code-Mixed Environment: A Case Study with Government Portals0
A House United: Bridging the Script and Lexical Barrier between Hindi and Urdu0
Whose Nickname is This? Recognizing Politicians from Their Aliases0
Improving Document Ranking using Query Expansion and Classification Techniques for Mixed Script Information Retrieval0
Romanized Berber and Romanized Arabic Automatic Language Identification Using Machine Learning0
CamelParser: A system for Arabic Syntactic Analysis and Morphological Disambiguation0
A Simple but Effective Approach to Improve Arabizi-to-English Statistical Machine Translation0
Query Translation for Cross-Language Information Retrieval using Multilingual Word Clusters0
YAMAMA: Yet Another Multi-Dialect Arabic Morphological Analyzer0
False-Friend Detection and Entity Matching via Unsupervised Transliteration0
Phonologically Aware Neural Model for Named Entity Recognition in Low Resource Transfer Settings0
Sequence-to-sequence neural network models for transliterationCode0
Neural Machine Transliteration: Preliminary Results0
Transliteration in Any Language with Surrogate Languages0
NRC Russian-English Machine Translation System for WMT 20160
Substring-based unsupervised transliteration with phonetic and contextual knowledge0
Target-Bidirectional Neural Models for Machine Transliteration0
Leveraging Entity Linking and Related Language Projection to Improve Name Transliteration0
The AFRL-MITLL WMT16 News-Translation Task Systems0
Linguistic Issues in the Machine Transliteration of Chinese, Japanese and Arabic Names0
Moses-based official baseline for NEWS 20160
Report of NEWS 2016 Machine Transliteration Shared Task0
Regulating Orthography-Phonology Relationship for English to Thai Transliteration0
Applying Neural Networks to English-Chinese Named Entity Transliteration0
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