Transliteration
45 papers with code • 0 benchmarks • 5 datasets
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.
Benchmarks
These leaderboards are used to track progress in Transliteration
Dakshina
TSAC
Most implemented papers
Neural Machine Translation Techniques for Named Entity Transliteration
Transliterating named entities from one language into another can be approached as neural machine translation (NMT) problem, for which we use deep attentional RNN encoder-decoder models.
Design Challenges in Named Entity Transliteration
steveash/NETransliteration-COLING2018
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COLING 2018
We analyze some of the fundamental design challenges that impact the development of a multilingual state-of-the-art named entity transliteration system, including curating bilingual named entity datasets and evaluation of multiple transliteration methods.
Bootstrapping Transliteration with Constrained Discovery for Low-Resource Languages
shyamupa/hma-translit
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EMNLP 2018
Generating the English transliteration of a name written in a foreign script is an important and challenging step in multilingual knowledge acquisition and information extraction.
Efficient Sequence Labeling with Actor-Critic Training
We set out to establish RNNs as an attractive alternative to CRFs for sequence labeling.
A Rule-based Kurdish Text Transliteration System
In this article, we present a rule-based approach for transliterating two mostly used orthographies in Sorani Kurdish.
Event detection in Twitter: A keyword volume approach
In this paper, we propose an efficient method to select the keywords frequently used in Twitter that are mostly associated with events of interest such as protests.
ANETAC: Arabic Named Entity Transliteration and Classification Dataset
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.
A Multi-cascaded Deep Model for Bilingual SMS Classification
Our model achieves high accuracy for classification on this dataset and outperforms the previous model for multilingual text classification, highlighting language independence of McM.
