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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 76–100 of 435 papers

TitleStatusHype
Composing RNNs and FSTs for Small Data: Recovering Missing Characters in Old Hawaiian Text—0
TArC: Tunisian Arabish Corpus First complete release—0
Non-Linear Pairwise Language Mappings for Low-Resource Multilingual Acoustic Model Fusion—0
Words.hk: A Comprehensive Cantonese Dictionary Dataset with Definitions, Translations and Transliterated Examples—0
A Digital Swedish-Yiddish/Yiddish-Swedish Dictionary: A Web-Based Dictionary that is also Available Offline—0
TArC: Tunisian Arabish Corpus, First complete release—0
CLeLfPC: a Large Open Multi-Speaker Corpus of French Cued Speech—0
From Inscription to Semi-automatic Annotation of Maya Hieroglyphic Texts—0
HindiWSD: A package for word sense disambiguation in Hinglish & Hindi—0
Extensions to Brahmic script processing within the Nisaba library: new scripts, languages and utilities—0
Exploiting Transliterated Words for Finding Similarity in Inter-Language News Articles using Machine Learning—0
Use of Transformer-Based Models for Word-Level Transliteration of the Book of the Dean of Lismore—0
DE-ABUSE@TamilNLP-ACL 2022: Transliteration as Data Augmentation for Abuse Detection in Tamil—0
UMUTeam@LT-EDI-ACL2022: Detecting homophobic and transphobic comments in Tamil—0
Joint Transformer/RNN Architecture for Gesture Typing in Indic Languages—0
Multilingual Abusiveness Identification on Code-Mixed Social Media Text—0
Does Transliteration Help Multilingual Language Modeling?Code0
Learning to pronounce as measuring cross-lingual joint orthography-phonology complexity—0
Towards a Broad Coverage Named Entity Resource: A Data-Efficient Approach for Many Diverse Languages—0
DEEP: DEnoising Entity Pre-training for Neural Machine Translation—0
English-to-Chinese Transliteration with Phonetic Back-transliteration—0
ARGUABLY at ComMA@ICON: Detection of Multilingual Aggressive, Gender Biased, and Communally Charged Tweets Using Ensemble and Fine-Tuned IndicBERT—0
Speech Synthesis for Low Resource Languages using Transliteration Enabled Transfer Learning—0
IIITT@Dravidian-CodeMix-FIRE2021: Transliterate or translate? Sentiment analysis of code-mixed text in Dravidian languagesCode0
DEEP: DEnoising Entity Pre-training for Neural Machine Translation—0
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