This work presents a comparative evaluation among three different Spanish segmentation strategies for Spanish-Chinese transliteration. The transliteration task is implemented by means of Statistical Machine Translation, using Chinese characters and Spanish sub-word segments as the textual units to be translated. Three different Spanish segmentation strategies are evaluated: character-based, syl...

Transliteration is defined as phonetic translation of names across languages. Transliteration of Named Entities (NEs) is necessary in many applications, such as machine translation, corpus alignment, cross-language IR, information extraction and automatic lexicon acquisition. All such systems call for high-performance transliteration, which is the focus of shared task in the NEWS 2016 workshop....

Transliteration is a set of cross-language conversion tasks widely used in many areas of natural language processing. Well known examples of transliteration tasks include letterto-phoneme conversion, entity linking, and out-of-vocabulary words transliteration in endto-end translation system. With this popularity, this review aims to provide a general overview of transliteration models proposed ...

While past research on machine transliteration has focused on a single transliteration task, there exist a variety of supplemental transliterations available in other languages. Given an input for English-toHindi transliteration, for example, transliterations from other languages such as Japanese or Hebrew may be helpful in the transliteration process. In this paper, we propose the application ...

Transliteration has been usually recognized by spelling-based supervised models. However, a single model cannot deal with mixture of words with different origins, such as “get” in “piaget” and “target”. Li et al. (2007) propose a class transliteration method, which explicitly models the source language origins and switches them to address this issue. In contrast to their model which requires an...

In this paper, we describe our approach to English-to-Korean transliteration task in NEWS 2012. Our system mainly consists of two components: an letter-to-phoneme alignment with m2m-aligner,and transliteration training model DirecTL-p. We construct different parameter settings to train several transliteration models. Then, we use two reranking methods to select the best transliteration among th...

This report documents the details of the Machine Transliteration Shared Task conducted as a part of the Named Entities Workshop (NEWS), an ACL-IJCNLP 2009 workshop. The shared task features machine transliteration of proper names from English to a set of languages. This shared task has witnessed enthusiastic participation of 31 teams from all over the world, with diversity of participation for ...

One of the important applications of Natural Language Processing is machine translation. Machine transliteration is an emerging and a very important research area in the field of machine translation. Translation systems translate message from source language to target language, keeping the exact meaning. While the transliteration system finds the same meaning word/sentence in another language, ...

This paper studies transliteration alignment, its evaluation metrics and applications. We propose a new evaluation metric, alignment entropy, grounded on the information theory, to evaluate the alignment quality without the need for the gold standard reference and compare the metric with F -score. We study the use of phonological features and affinity statistics for transliteration alignment at...

This paper presents transliteration mining on the ACL 2010 NEWS workshop shared transliteration mining task data. Transliteration mining was done using a generative transliteration model applied on the source language and whose output was constrained on the words in the target language. A total of 30 runs were performed on 5 language pairs, with 6 runs for each language pair. In the presence of...