Improving E nglish- A rabic Transliteration with Phonemic Memories (2022)

Abstract: Transliteration is an important task in natural language processing (NLP) which aims to convert a name in the source language to the target language without changing its pronunciation. Particularly, transliteration from English to Arabic is highly needed in many applications, especially in countries (e.g., United Arab Emirates (UAE)) whose most citizens are foreigners but the official language is Arabic. In such a task-oriented scenario, namely transliterating the English names to the corresponding Arabic ones, the performance of the transliteration model is highly important. However, most existing neural approaches mainly apply a universal transliteration model with advanced encoders and decoders to the task, where limited attention is paid to leveraging the phonemic association between English and Arabic to further improve model performance. In this paper, we focus on transliteration of people’s names from English to Arabic for the general public. In doing so, we collect a corpus named EANames by extracting high quality name pairs from online resources which better represent the names in the general public than linked Wikipedia entries that are always names of famous people). We propose a model for English-Arabic transliteration, where a memory module modeling the phonemic association between English and Arabic is used to guide the transliteration process. We run experiments on the collected data and the results demonstrate the effectiveness of our approach for English-Arabic transliteration.

## introduction with the rapid development of globalization, the number of people working and living cross countries has been significantly grown in the past † corresponding author. 1 the source of the paper is available under https:// github.com/synlp/eatrans . conventional studies use grapheme-based, phoneme-based, and hybrid approaches to learn the mapping of the features and phonemes between the source and target languages @xcite @xcite @xcite @xcite . recently, end-to-end neural approaches are also applied to transliteration tasks @xcite @xcite @xcite and achieve good performance. among these studies, most neural approaches focus more on using advanced encoders and decoders (such as rnn, gru, lstm, and transformer @xcite ) following the standard sequence-to-sequence paradigm, where less attention is paid to leverage the phonemic information of the source and target languages to improve model performance, especially for transliteration from english to arabic. in this paper, we focus on transliteration from english to arabic foot_0 for the general public. to train a model, it is intuitive to directly use existing corpora for named entity transliteration @xcite @xcite @xcite . however, many of those corpora are not public available and most of them are constructed by extracting the named entity pairs from linked wikipedia entries without distinguishing different types of named entities. therefore, the instances from the other types of named entities (e.g., locations) could introduce noise to a model that is designed mainly for name transliteration. more importantly, since the names in wikipedia are more likely to come from famous people, they may fail to represent the names in the general public. as a result, models trained with existing corpora may not be satisfying in real applications (e.g., transliterate a migrant's name from english to arabic). therefore, we collect a corpus named eanames for english-arabic transliteration. the training and development data of eanames are obtained by crawling english names from the profile of linkedin foot_1 users who are working in a representative arabic speaking contrary, i.e., uae, and the corresponding arabic ones are obtained from well-known transliteration systems and human annotation. the test set contains 3,000 english-arabic name pairs extracted from large english-arabic parallel news corpus with their quality justified by well-known transliteration systems and human annotation. besides, we propose a neural sequence-tosequence model following the encoder-decoder paradigm for english-arabic transliteration, which is enhanced by phonemic memories that are designed to memorize the phonemic association between english and arabic names and thus guide the transliteration process. specifically, for each input english letter, we firstly associate it with the phonemic symbols extracted from a phoneme inventory constructed based on the phonetic systems of english and arabic. next, in the memory module, we weigh the associated symbols according to their contribution to the transliteration process and incorporate the weighed information into the backbone encoder-decoder model. we run experiments with different models on eanames, where our model outperforms strong baselines and achieves the bast results under different metrics on the test set. ## the eanames corpus generally, it is significantly important to train and evaluate models on data that is similar to the ones in real applications. however, most public available transliteration corpora are constructed from associated wikipedia entity entries in different languages and thus are more representative to names of famous people rather than the general public. therefore, instead of using existing corpora for english-arabic transliteration, we collect a corpus named eanames with english names and their arabic counterparts collected from online resources and human annotation. in doing so, we use different approaches to extract training and test data because they have various requirements on the quantity and quality. generally, the quantity of training data is much more important, where some noises in it would not significantly hurt the performance of a model trained on it; on the contrary, test data requires more on the quality with limited tolerance on mislabeled data. in the following text, we illustrate our approaches to extract the training and test data and finally report the statistics of the collected eanames. ## the approach in this paper, we propose a neural model for transliteration from english to arabic, where the architecture is illustrated in figure 2 . overall, our approach follows the sequence-to-sequence paradigm for text generation, where phonemic memories are proposed and added on the top of every encoder layer to enhance the model performance by modeling the phonemic information extracted from a phonemic inventory v. at the time step t, we denote the input english name as @xmath0. therefore, the object of our transliteration model is to predict the next arabic letter y t through ## related work transliteration is an important task that is relevant to translation and has been studied for decades. conventional approaches for transliteration are categorized into grapheme-based, phoneme-based, and hybrid approaches to learn the phonemic connections between the sounds of the source and target languages @xcite @xcite @xcite @xcite @xcite . these approaches usually contain the following steps. first, the text in the source language is converted into the sounds in the source language. then, the sounds are modified to and transformer (b). "full model" denotes our model with the attention mechanism to leverage phonemic information; "-att." refers to the model where the attention is ablated. we also report the results of standard lstm and transformer baselines for reference. compared with previous studies, the name pairs in eanames are collected online sources rather than wikipedia entries and the proposed neural approach for english-arabic transliteration uses a memory-based module to leverage the language specific phonemic information. ## conclusions in this paper, we collect a corpus named eanames for english-arabic transliteration, where the names pairs are collected from online resources rather than wikipedia entries. based on the real data from eanames, we propose a neural transliteration model enhanced by memories to take advantage of phonemic information from english and arabic. specifically, in the memory module, the phonemic symbols associated with each input english letter are weighed and leveraged discriminatively to guide the transliteration process from english to arabic. the experimental results and analysis on eanames demonstrate the effectiveness of our approach, which outperforms strong baselines with respect to all widely used evaluation metrics.