Transliteration
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 0
- 보존된 평가 기록
- 0
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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/vrclc-duk/ml-en-transliteration
공식 코드Romanized to Native Malayalam Script Transliteration Using an Encoder-Decoder Framework
https://github.com/yashkhem1/RelateLM
공식 코드https://github.com/Aso-UniMelb/neural-fieldwork-guide
https://github.com/ai4bharat/indicllmsuite
Aksharantar: Open Indic-language Transliteration datasets and models for the Next Billion Users
https://github.com/shyamupa/hma-translit
Bootstrapping Transliteration with Constrained Discovery for Low-Resource Languages
https://github.com/sigmorphon/2022inflectionst
https://github.com/souvikshanku/translit-former
Processing South Asian Languages Written in the Latin Script: the Dakshina Dataset
https://github.com/thunlp/readin
Sub-Character Tokenization for Chinese Pretrained Language Models
https://github.com/vsatyav007/repo-eventdetection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.