태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
The Dakshina dataset is a collection of text in both Latin and native scripts for 12 South Asian languages. For each language, the dataset includes a large collection of native script Wikipedia text, a romanization lexicon which consists of words in the native script with attested romanizations, and some full sentence parallel data in both a native script of the language and the basic Latin alphabet. Source: Google Research
Tunisian Sentiment Analysis Corpus (TSAC) is a Tunisian Dialect corpus of 17.000 comments from Facebook. Source: Sentiment Analysis of Tunisian Dialects: Linguistic Ressources and Experiments
Corpus of Egyptian Arabic-English Code-switching (ArzEn) is a spontaneous conversational speech corpus, obtained through informal interviews held at the German University in Cairo. The participants discussed broad topics, including education, hobbies, work, and life experiences. The corpus currently contains 12 hours of speech, having 6,216 utterances. The recordings were transcribed and translated into monolingual Egyptian Arabic and monolingual English. Source: https://arxiv.org/pdf/2211.16319v1.pdf
An English-Arabic named entity transliteration and classification dataset built from freely available parallel translation corpora. The dataset contains 79,924 instances, each instance is a triplet (e, a, c), where e is the English named entity, a is its Arabic transliteration and c is its class that can be either a Person, a Location, or an Organization. The ANETAC dataset is mainly aimed for the researchers that are working on Arabic named entity transliteration, but it can also be used for named entity classification purposes. Source: ANETAC: Arabic Named Entity Transliteration and Classification Dataset
Bianet is a parallel news corpus in Turkish, Kurdish and English It contains 3,214 Turkish articles with their sentence-aligned Kurdish or English translations from the Bianet online newspaper. Source: Bianet: A Parallel News Corpus in Turkish, Kurdish and English
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.