Transliterations play an important role in multilingual entity reference resolution, because proper names increasingly travel between languages in news and social media. Previous work associated with machine translation targets transliteration only single between language pairs, focuses on specific classes of entities (such as cities and celebrities) and relies on manual curation, which limits the expression power of transliteration in multilingual environment. By contrast, we present an unsupervised transliteration model covering 69 major languages that can generate good transliterations for arbitrary strings between any language pair. Our model yields top-(1, 20, 100) averages of (32.85 , 60.44 , 83.20 ) in matching gold standard transliteration compared to results from a recently-published system of (26.71 , 50.27 , 72.79 ). We also show the quality of our model in detecting true and false friends from Wikipedia high frequency lexicons. Our method indicates a strong signal of pronunciation similarity and boosts the probability of finding true friends in 68 out of 69 languages.
We address the problem of transliterating English names using Chinese orthography in support of cross-lingual speech and text processing applications. We demonstrate the application of statistical machine translation techniques to "translate" the phonemic representation of an English name, obtained by using an automatic text-to-speech system, to a sequence of initials and finals, commonly used sub-word units of pronunciation for Chinese. We then use another statistical translation model to map the initial final sequence to Chinese characters. We also present an evaluation of this module in retrieval of Mandarin spoken documents from the TDT corpus using English text queries. This paper presents a hybrid approach to English-Korean name transliteration. The base system is built on MOSES with enabled factored translation features. We expand the base system by combining with various transliteration methods including a Web-based n-best re-ranking, a dictionary-based method, and a rule-based method. Our standard run and best non-standard run achieve 45.1 and 78.5, respectively, in top-1 accuracy. Experimental results show that expanding training data size significantly contributes to the performance. Also we discover that the Web-based re-ranking method can be successfully applied to the English-Korean transliteration.  In this paper, we address the problem of building a multilingual transliteration system using an interlingual representation. Our approach uses international phonetic alphabet (IPA) to learn the interlingual representation and thus allows us to use any word and its IPA representation as a training example. Thus, our approach requires only monolingual resources: a phoneme dictionary that lists words and their IPA representations. By adding a phoneme dictionary of a new language, we can readily build a transliteration system into any of the existing previous languages, without the expense of all-pairs data or computation. We also propose a regularization framework for learning the interlingual representation, which accounts for language specific phonemic variability, and thus it can find better mappings between languages. Experimental results on the name transliteration task in five diverse languages show a maximum improvement of 29 accuracy and an average improvement of 17 accuracy compared to a state-of-the-art baseline system. We present a Hebrew to English transliteration method in the context of a machine translation system. Our method uses machine learning to determine which terms are to be transliterated rather than translated. The training corpus for this purpose includes only positive examples, acquired semi-automatically. Our classifier reduces more than 38 of the errors made by a baseline method. The identified terms are then transliterated. We present an SMT-based transliteration model trained with a parallel corpus extracted from Wikipedia using a fairly simple method which requires minimal knowledge. The correct result is produced in more than 76 of the cases, and in 92 of the instances it is one of the top-5 results. We also demonstrate a small improvement in the performance of a Hebrew-to-English MT system that uses our transliteration module. Transliteration is the rendering in one language of terms from another language (and, possibly, another writing system), approximating spelling and or phonetic equivalents between the two languages. A transliteration dictionary is a crucial resource for a variety of natural language applications, most notably machine translation. We describe a general method for creating bilingual transliteration dictionaries from Wikipedia article titles. The method can be applied to any language pair present on Wikipedia, independently of the writing systems involved, and requires only a single simple resource that can be provided by any literate bilingual speaker. It was successfully applied to extract a Hebrew-English transliteration dictionary which was used to train a transliteration module that, when incorporated in a machine translation system, indeed improved its performance.   A proper noun dictionary is never complete rendering name translation from English to Chinese ineffective. One way to solve this problem is not to rely on a dictionary alone but to adopt automatic translation according to pronunciation similarities, i.e. to map phonemes comprising an English name to the phonetic representations of the corresponding Chinese name. This process is called transliteration. We present a statistical transliteration method. An efficient algorithm for aligning phoneme chunks is described. Unlike rule-based approaches, our method is data-driven. Compared to source-channel based statistical approaches, we adopt a direct transliteration model, i.e. the direction of probabilistic estimation conforms to the transliteration direction. We demonstrate comparable performance to source-channel based system. Out of vocabulary (OOV) words are problematic for cross language information retrieval. One way to deal with OOV words when the two languages have different alphabets, is to transliterate the unknown words, that is, to render them in the orthography of the second language. In the present study, we present a simple statistical technique to train an English to Arabic transliteration model from pairs of names. We call this a selected n-gram model because a two-stage training procedure first learns which n-gram segments should be added to the unigram inventory for the source language, and then a second stage learns the translation model over this inventory. This technique requires no heuristics or linguistic knowledge of either language. We evaluate the statistically-trained model and a simpler hand-crafted model on a test set of named entities from the Arabic AFP corpus and demonstrate that they perform better than two online translation sources. We also explore the effectiveness of these systems on the TREC 2002 cross language IR task. We find that transliteration either of OOV named entities or of all OOV words is an effective approach for cross language IR.