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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 401435 of 435 papers

TitleStatusHype
Does Transliteration Help Multilingual Language Modeling?Code0
Role of Language Relatedness in Multilingual Fine-tuning of Language Models: A Case Study in Indo-Aryan LanguagesCode0
A Multi-cascaded Deep Model for Bilingual SMS ClassificationCode0
Sequence-to-sequence neural network models for transliterationCode0
Design Challenges in Named Entity TransliterationCode0
A Large-scale Evaluation of Neural Machine Transliteration for Indic LanguagesCode0
A Rule-based Kurdish Text Transliteration SystemCode0
Efficient Sequence Labeling with Actor-Critic TrainingCode0
Romanized to Native Malayalam Script Transliteration Using an Encoder-Decoder FrameworkCode0
Creating Large-Scale Multilingual Cognate TablesCode0
TransliCo: A Contrastive Learning Framework to Address the Script Barrier in Multilingual Pretrained Language ModelsCode0
An Empirical Study of Chinese Name Matching and ApplicationsCode0
On Biasing Transformer Attention Towards MonotonicityCode0
Jailbreaking LLMs with Arabic Transliteration and ArabiziCode0
Creating a Translation Matrix of the Bible's Names Across 591 LanguagesCode0
Bootstrapping Transliteration with Constrained Discovery for Low-Resource LanguagesCode0
Sinhala Transliteration: A Comparative Analysis Between Rule-based and Seq2Seq ApproachesCode0
How Grammatical is Character-level Neural Machine Translation? Assessing MT Quality with Contrastive Translation PairsCode0
Towards Offensive Language Identification for Tamil Code-Mixed YouTube Comments and PostsCode0
Event detection in Twitter: A keyword volume approachCode0
Exploiting Language Relatedness for Low Web-Resource Language Model Adaptation: An Indic Languages StudyCode0
How Transliterations Improve Crosslingual AlignmentCode0
Universal Dependency Parsing for Hindi-English Code-switchingCode0
Orthographic Transliteration for Kabyle Speech RecognitionCode0
Specializing Multilingual Language Models: An Empirical StudyCode0
Cross-Lingual Text Classification of Transliterated Hindi and MalayalamCode0
Context Independent Term Mapper for European LanguagesCode0
IIITT@Dravidian-CodeMix-FIRE2021: Transliterate or translate? Sentiment analysis of code-mixed text in Dravidian languagesCode0
Can Small Language Models Learn, Unlearn, and Retain Noise Patterns?Code0
Neural Machine Translation Techniques for Named Entity TransliterationCode0
Breaking the Script Barrier in Multilingual Pre-Trained Language Models with Transliteration-Based Post-Training AlignmentCode0
TransMI: A Framework to Create Strong Baselines from Multilingual Pretrained Language Models for Transliterated DataCode0
Neural String Edit DistanceCode0
Bilingual dictionaries for all EU languagesCode0
Towards Offensive Language Identification for Dravidian LanguagesCode0
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