Transliteration
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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.
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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
TransliCo: A Contrastive Learning Framework to Address the Script Barrier in Multilingual Pretrained Language Models
Language Detection for Transliterated Content
Code-Mixed Text to Speech Synthesis under Low-Resource Constraints
Character-Level Bangla Text-to-IPA Transcription Using Transformer Architecture with Sequence Alignment
BenLLMEval: A Comprehensive Evaluation into the Potentials and Pitfalls of Large Language Models on Bengali NLP
Exploring Linguistic Similarity and Zero-Shot Learning for Multilingual Translation of Dravidian Languages
Show Me the World in My Language: Establishing the First Baseline for Scene-Text to Scene-Text Translation
Taqyim: Evaluating Arabic NLP Tasks Using ChatGPT Models
Multilingual Neural Machine Translation System for Indic to Indic Languages
Learning Cross-lingual Mappings for Data Augmentation to Improve Low-Resource Speech Recognition
DeepScribe: Localization and Classification of Elamite Cuneiform Signs Via Deep Learning
Multilingual Text-to-Speech Synthesis for Turkic Languages Using Transliteration
XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages
Towards Transliteration between Sindhi Scripts from Devanagari to Perso-Arabic
Investigating Lexical Sharing in Multilingual Machine Translation for Indian Languages
Romanization-based Large-scale Adaptation of Multilingual Language Models
Unsupervised Language agnostic WER Standardization
Beyond Arabic: Software for Perso-Arabic Script Manipulation
EPIK: Eliminating multi-model Pipelines with Knowledge-distillation
Benchmarking Evaluation Metrics for Code-Switching Automatic Speech Recognition
Towards Zero-Shot Code-Switched Speech Recognition
DuDe: Dual-Decoder Multilingual ASR for Indian Languages using Common Label Set
Gui at MixMT 2022 : English-Hinglish: An MT approach for translation of code mixed data
MATra: A Multilingual Attentive Transliteration System for Indian Scripts
Composing RNNs and FSTs for Small Data: Recovering Missing Characters in Old Hawaiian Text
TArC: Tunisian Arabish Corpus First complete release
Non-Linear Pairwise Language Mappings for Low-Resource Multilingual Acoustic Model Fusion
Exploiting Transliterated Words for Finding Similarity in Inter-Language News Articles using Machine Learning
Use of Transformer-Based Models for Word-Level Transliteration of the Book of the Dean of Lismore
A machine transliteration tool between Uzbek alphabets
Aksharantar: Open Indic-language Transliteration datasets and models for the Next Billion Users
Joint Transformer/RNN Architecture for Gesture Typing in Indic Languages
Multilingual Abusiveness Identification on Code-Mixed Social Media Text
ParaNames: A Massively Multilingual Entity Name Corpus
Does Transliteration Help Multilingual Language Modeling?
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.