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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 201–250 of 435 papers

TitleStatusHype
Portable Spelling Corrector for a Less-Resourced Language: Amharic—0
The MADAR Arabic Dialect Corpus and Lexicon—0
Creating Large-Scale Multilingual Cognate TablesCode0
A Bird's-eye View of Language Processing Projects at the Romanian Academy—0
A Comparative Study of Extremely Low-Resource Transliteration of the World's Languages—0
Palmyra: A Platform Independent Dependency Annotation Tool for Morphologically Rich Languages—0
Creating a Translation Matrix of the Bible's Names Across 591 LanguagesCode0
The French-Algerian Code-Switching Triggered audio corpus (FACST)—0
Universal Dependency Parsing for Hindi-English Code-switchingCode0
Putting Figures on Influences on Moroccan Darija from Arabic, French and Spanish using the WordNet—0
Leveraging Orthographic Similarity for Multilingual Neural Transliteration—0
Sequence to Sequence Networks for Roman-Urdu to Urdu Transliteration—0
A Neural Network Transliteration Model in Low Resource Settings—0
PJIIT's systems for WMT 2017 Conference—0
Transliterated Mobile Keyboard Input via Weighted Finite-State Transducers—0
Learning Bilingual Projections of Embeddings for Vocabulary Expansion in Machine Translation—0
Joint Prediction of Morphosyntactic Categories for Fine-Grained Arabic Part-of-Speech Tagging Exploiting Tag Dictionary Information—0
HCCL at SemEval-2017 Task 2: Combining Multilingual Word Embeddings and Transliteration Model for Semantic Similarity—0
Statistical Models for Unsupervised, Semi-Supervised Supervised Transliteration Mining—0
Arabic Diacritization: Stats, Rules, and Hacks—0
Robust Dictionary Lookup in Multiple Noisy Orthographies—0
A Layered Language Model based Hybrid Approach to Automatic Full Diacritization of Arabic—0
Neural Machine Translation on Scarce-Resource Condition: A case-study on Persian-English—0
A Universal Dependencies Treebank for Marathi—0
How Grammatical is Character-level Neural Machine Translation? Assessing MT Quality with Contrastive Translation PairsCode0
YAMAMA: Yet Another Multi-Dialect Arabic Morphological Analyzer—0
CamelParser: A system for Arabic Syntactic Analysis and Morphological Disambiguation—0
A House United: Bridging the Script and Lexical Barrier between Hindi and Urdu—0
Opinion Mining in a Code-Mixed Environment: A Case Study with Government Portals—0
Romanized Berber and Romanized Arabic Automatic Language Identification Using Machine Learning—0
Improving Document Ranking using Query Expansion and Classification Techniques for Mixed Script Information Retrieval—0
Query Translation for Cross-Language Information Retrieval using Multilingual Word Clusters—0
A Simple but Effective Approach to Improve Arabizi-to-English Statistical Machine Translation—0
Whose Nickname is This? Recognizing Politicians from Their Aliases—0
False-Friend Detection and Entity Matching via Unsupervised Transliteration—0
Phonologically Aware Neural Model for Named Entity Recognition in Low Resource Transfer Settings—0
Sequence-to-sequence neural network models for transliterationCode0
Transliteration in Any Language with Surrogate Languages—0
Neural Machine Transliteration: Preliminary Results—0
Moses-based official baseline for NEWS 2016—0
IXA Biomedical Translation System at WMT16 Biomedical Translation Task—0
PJAIT Systems for the WMT 2016—0
Target-Bidirectional Neural Models for Machine Transliteration—0
The AFRL-MITLL WMT16 News-Translation Task Systems—0
Leveraging Entity Linking and Related Language Projection to Improve Name Transliteration—0
Linguistic Issues in the Machine Transliteration of Chinese, Japanese and Arabic Names—0
A Multilinear Approach to the Unsupervised Learning of Morphology—0
Whitepaper of NEWS 2016 Shared Task on Machine Transliteration—0
NRC Russian-English Machine Translation System for WMT 2016—0
Applying Neural Networks to English-Chinese Named Entity Transliteration—0
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