This paper presents our machine transliteration systems developed for the NEWS 2015 machine transliteration shared task. Our systems are applied to two tasks: English to Chinese and Chinese to English. For standard runs, in which only official data sets are used, we build phrase-based transliteration models with refined alignments provided by the M2M-aligner. For non-standard runs, we add multi...

We propose a new method of selecting hypotheses for machine transliteration. We generate a set of Chinese, Japanese, and Korean transliteration hypotheses for a given English word. We then use the set of transliteration hypotheses as a guide to finding relevant Web pages and mining contextual information for the transliteration hypotheses from the Web page. Finally, we use the mined information...

We present a transliteration system that introduces minimum description length training for transliteration and combines it with discriminative modeling. We apply the proposed approach to transliteration from English to 8 non-Latin scripts, with promising results.

Transliteration is the general choice for handling named entities and out of vocabulary words in any MT application, particularly in machine translation. Transliteration (or forward transliteration) is the process of mapping source language phonemes or graphemes into target language approximations; the reverse process is called back transliteration. This paper presents a novel approach to impro...

Transliteration is the conversion of a text from one script to another, and thus representing words from one language using the approximate phonetic or spelling equivalents of another language. Machine Transliteration has come out to be an emerging and a very important research area in the field of machine translation. Transliteration systems are very beneficial for removing the language and sc...

We propose a novel algorithm for English to Persian transliteration. Previous methods proposed for this language pair apply a word alignment tool for training. By contrast, we introduce an alignment algorithm particularly designed for transliteration. Our new model improves the English to Persian transliteration accuracy by 14% over an n-gram baseline. We also propose a novel back-transliterati...

Several models for transliteration pair acquisition have been proposed to overcome the out-of-vocabulary problem caused by transliterations. To date, however, there has been little literature regarding a framework that can accommodate several models at the same time. Moreover, there is little concern for validating acquired transliteration pairs using up-to-date corpora, such as web documents. ...

In this paper, we discuss our endeavors for the Named Entities Workshop (NEWS) 2016 transliteration shared task, where we focus on English to Thai transliteration. The alignment between Thai orthography and phonology is not always monotonous, but few transliteration systems take this into account. In our proposed system, we exploit phonological knowledge to resolve problematic instances where t...

This article describes the first trial on bidirectional Thai-English machine transliteration applied on the NEWS 2010 transliteration corpus. The system relies on segmenting sourcelanguage words into syllable-like units, finding unit's pronunciations, consulting a syllable transliteration table to form target-language word hypotheses, and ranking the hypotheses by using syllable n-gram. The app...

We investigate three methods for integrating an unsupervised transliteration model into an end-to-end SMT system. We induce a transliteration model from parallel data and use it to translate OOV words. Our approach is fully unsupervised and language independent. In the methods to integrate transliterations, we observed improvements from 0.23-0.75 (∆ 0.41) BLEU points across 7 language pairs. We...