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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
A Comparative Study of Extremely Low-Resource Transliteration of the World's Languages—0
Creating Large-Scale Multilingual Cognate TablesCode0
A Bird's-eye View of Language Processing Projects at the Romanian Academy—0
Portable Spelling Corrector for a Less-Resourced Language: Amharic—0
Palmyra: A Platform Independent Dependency Annotation Tool for Morphologically Rich Languages—0
The French-Algerian Code-Switching Triggered audio corpus (FACST)—0
Automatic Identification of Maghreb Dialects Using a Dictionary-Based Approach—0
The MADAR Arabic Dialect Corpus and Lexicon—0
The LIA Treebank of Spoken Norwegian Dialects—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
PJIIT's systems for WMT 2017 Conference—0
Transliterated Mobile Keyboard Input via Weighted Finite-State Transducers—0
A Neural Network Transliteration Model in Low Resource Settings—0
HCCL at SemEval-2017 Task 2: Combining Multilingual Word Embeddings and Transliteration Model for Semantic Similarity—0
Joint Prediction of Morphosyntactic Categories for Fine-Grained Arabic Part-of-Speech Tagging Exploiting Tag Dictionary Information—0
Learning Bilingual Projections of Embeddings for Vocabulary Expansion in Machine Translation—0
Statistical Models for Unsupervised, Semi-Supervised Supervised Transliteration Mining—0
A Layered Language Model based Hybrid Approach to Automatic Full Diacritization of Arabic—0
Arabic Diacritization: Stats, Rules, and Hacks—0
Robust Dictionary Lookup in Multiple Noisy Orthographies—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
Opinion Mining in a Code-Mixed Environment: A Case Study with Government Portals—0
A House United: Bridging the Script and Lexical Barrier between Hindi and Urdu—0
Whose Nickname is This? Recognizing Politicians from Their Aliases—0
Improving Document Ranking using Query Expansion and Classification Techniques for Mixed Script Information Retrieval—0
Romanized Berber and Romanized Arabic Automatic Language Identification Using Machine Learning—0
CamelParser: A system for Arabic Syntactic Analysis and Morphological Disambiguation—0
A Simple but Effective Approach to Improve Arabizi-to-English Statistical Machine Translation—0
Query Translation for Cross-Language Information Retrieval using Multilingual Word Clusters—0
YAMAMA: Yet Another Multi-Dialect Arabic Morphological Analyzer—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
Neural Machine Transliteration: Preliminary Results—0
Transliteration in Any Language with Surrogate Languages—0
NRC Russian-English Machine Translation System for WMT 2016—0
Substring-based unsupervised transliteration with phonetic and contextual knowledge—0
Target-Bidirectional Neural Models for Machine Transliteration—0
Leveraging Entity Linking and Related Language Projection to Improve Name Transliteration—0
The AFRL-MITLL WMT16 News-Translation Task Systems—0
Linguistic Issues in the Machine Transliteration of Chinese, Japanese and Arabic Names—0
Moses-based official baseline for NEWS 2016—0
Report of NEWS 2016 Machine Transliteration Shared Task—0
Regulating Orthography-Phonology Relationship for English to Thai Transliteration—0
Applying Neural Networks to English-Chinese Named Entity Transliteration—0
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