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Using Transliteration of Proper Names from Arabic to Latin Script to Improve English-Arabic Word Alignment

Bilingual lexicons of proper names play a vital role in machine translation and cross-language information retrieval. Word alignment approaches are generally used to construct bilingual lexicons automatically from parallel corpora. Aligning proper names is a task particularly difficult when the source and target languages of the parallel corpus do not share a same written script. We present in this paper a system to transliterate automatically proper names from Arabic to Latin script, and a tool to align single and compound words from English-Arabic parallel texts. We particularly focus on the impact of using transliteration to improve the performance of the word alignment tool. We have evaluated the word alignment tool integrating transliteration of proper names from Arabic to Latin script using two methods: A manual evaluation of the alignment quality and an evaluation of the impact of this alignment on the translation quality by using the open source statistical machine translation system Moses. Experiments show that integrating transliteration of proper names into the alignment process improves the Fmeasure of word alignment from 72% to 81% and the translation BLEU score from 20.15% to 20.63%.


Introduction
Bilingual lexicons of proper names play a vital role in Machine Translation (MT) and Cross-Language Information Retrieval (CLIR). Word alignment approaches are generally used to construct bilingual lexicons automatically from parallel corpora. Aligning proper names requires both recognition of the proper names present in the parallel corpus and their alignment (Abuleil and Evens, 2004). This task is particularly difficult when the source and target languages of the parallel corpus do not share a same written script. A solution to this issue consists in writing the proper names present in the parallel corpus in the same written script. This operation is named transliteration and consists in replacing each grapheme of a writing system by another grapheme or a group of graphemes of another writing system, regardless of pronunciation.
In order to study the impact of using transliteration to improve the performance of a word alignment tool, we present in this paper a system to transliterate automatically proper names from Arabic to Latin script, and a tool to align single and compound words from English-Arabic parallel texts.
The remainder of the paper is organized as follows: Section 2 recalls in some previous work addressing tasks of transliteration and bilingual lexicon extraction from parallel corpora. In section 3, we present briefly the system for automatic transliteration of proper names from Arabic to Latin script. Section 4 describes the process of using transliteration in the word alignment tool. We present in section 5 the experimental protocol we followed and discuss the obtained results. We finally conclude and present directions for future work in section 6.

Related Work
In order to build bilingual lexicons from parallel corpora automatically, several word alignment approaches have been explored (Daille et al., 1994;Blank, 2000;Barbu, 2004). These approaches align proper names correctly when the source and target languages of the parallel corpus share a same written script. Recent research works for aligning proper names when the source and target languages do not share a same written script have focused on automatic alignment of transliterations in order to enrich bilingual lexicons of named entities. These include (Al-Onaizan and Knight, 2002) and (Sherif and Kondrak, 2007) who worked on the Arabic-English alignment, (Tao et al., 2006) who worked on Arabic, Chinese and English, and (Shao and Ng, 2004) who used the information resulted from transliterations based on pronunciation. They combined the obtained information from the translation context and those generated from Chinese and English transliteration. This technique allowed processing some specific infrequent words. Some other systems assign for a given name only one transliteration such as the generative model for English words written in Japanese (Katakana) to Latin transcription (Knight and Graehl, 1997). This approach was adapted by (Stalls and Knight, 1998) to translate English words written in Arabic into English. (AbdulJaleel and Larkey, 2003) proposed a system based on a statistical approach to transliterate English names into Arabic. This system has several limitations as it uses the computation of the most probable form supposed to be the correct one. Indeed, this hypothesis is not always valid in all the Arab countries and dialects. To avoid pronunciation and dialect varieties, (Alghamdi, 2005) proposed a system to transliterate vowelized Arabic names into English. This system is based on a dictionary of Arabic names in which the pronunciation is set using vowels added to listed names with an indication of their equivalents in English. This approach has a strong limitation when used in word alignment as it proposes only one transliteration for a given name. Recently, (Saadane et al., 2012) proposed an approach to transliterate proper names from Arabic to Latin script which takes into account phonological and linguistic aspects. The authors reported an improvement of the F-measure of their French-Arabic word alignment tool from 82% to 86%.

Transliteration of Proper Names from
Arabic to Latin Script The transliteration system of proper names from Arabic to Latin script used in this study (Saadane et al., 2012) is based on a finite-state automaton. This automaton switches from one state to another according to the outward transitions of the current state and the currently processed letter of the Arabic word. The transliteration process is composed of the following main steps: 1. Transliteration: Each proper name is, first, split or not into several elements according to its type and the particles which do not compose the name itself are transcribed. Then, transliteration rules are applied to transliterate the names themselves. These rules are applied in a certain order based on the number of consonants of the proper name. For example, the compound name " ‫ا‬ " is, first, split into " + ‫ال‬ + ‫,"ر‬ second, the particles " " and ‫"ال"‬ are transcribed into "abd" and "al", and finally the name " ‫"ر‬ is transliterated into rachid, rashid, etc.

Normalization: This step consists in per-
forming some post-processing on the generated transliterations such as changing the first letter into capital.
3. Weighting: This step consists in assigning weights to the rules used to generate the list of transliterations in order to display the results sorted from the most likely to the least likely. Results of some search engines are exploited to compute these weights based on the number of occurrences for each generated transliteration of the proper name.

Alignment of Proper Names from English-Arabic Corpora
Word alignment from parallel corpora consists, on the one hand, in identifying words present in the source and target texts, and, on the other hand, in establishing correspondences between these words. The word alignment tool evaluated in this study , first, identifies single words and compound words present in the parallel corpus using the linguistic analyzer LIMA (Besançon et al., 2010), and, second, establishes correspondence relations between these words using the following steps: 1. Look-up of words which are present in an existing English-Arabic lexicon composed of 149495 entries; 2. Matching of words which are cognates; 3. Matching of words which have the same grammatical categories; 4. Establishing correspondence relations between compound words.
We describe below only the step 2 which illustrates the process of using transliteration of proper names from Arabic to Latin script in English-Arabic word alignment.
Proper names alignment consists, first, in searching words present in the source and target sentences which have the grammatical category "Proper Name" by using the results of the linguistic analyzer LIMA, and, second, in identifying words which are cognates. Several research works have shown that using cognates can improve both sentence alignment (Simard et al., 1993) and word alignment (Kondrak, 2005). In our implementation, we consider, in a first step, that pairs of words which share the first four characters as cognates. This step uses the results of the transliteration into Latin script of all the proper names present in the Arabic corpus and can identify, for example, that the proper name "Kosovo" and the transliteration of the Arabic word " ‫"آ‬ ("kosoufou") are cognates. However, this step does not detect pairs of words such as "Algeria" and "aljazair" (transliteration of the Arabic word " ‫ا‬ ‫.)"ا‬ To take into account this kind of pairs of words, we used the Jaro-Winkler distance DJW (Winkler, 1990), a similarity measure based on the number of letters in common between the string of the word of the source language ws and the string of the word of the target language wt. where: • m is the number of matching characters. Two characters from ws and wt respectively, are considered matching only if they are the same and not farther than: • t is the number of transpositions which is equal to the half of number of characters in ws that do not line up (by index in the matched subsequence) with identical characters in wt. • |ws|, |wt| are lengths of the strings corresponding to the words ws and wt.
Jaro-Winkler similarity measure is a variant of the Jaro distance metric DJ (Jaro, 1989). where: • l is the length of common prefix at the start of the string up to a maximum of 4 characters. • p is a constant scaling factor for how much the score is adjusted upwards for having common prefixes.
In order to identify the values of l and p which provide the best alignment, we checked manually the result of the transliteration of 254 proper names. This evaluation showed that, if l is equal to 2 and p is equal to 0.1, the words ws and wt are cognates when the value of the Jaro-Winkler distance is the highest.  The word "Kosovo" was aligned using cognates matching after transliteration, the words "condemn", "human", "affect" and "group" were aligned using grammatical categories matching and the other single words exist in the English-Arabic lexicon. The compound words "viola-tion_human_right", ‫ن"‬ َ ْ ‫_إ‬ َ ‫ك_‬ َ ِ ْ ِ ‫,"إ‬ "hu-man_right", ‫ن"‬ َ ْ ‫_إ‬ َ ", "ethnic_group" and " ِ َ َ _ َ ِ " are first recognized by LIMA respectively from the source sentence and the target sentence, and then aligned using lexical and syntactic transfer rules between source and target languages (Ozdowska, 2004).

Experimental Results and Evaluation
The impact of using transliteration of proper names on the quality of alignment and machine translation has been evaluated according to the two following approaches: • A manual evaluation comparing the results of our word aligner with a reference alignment; • An automatic evaluation by integrating the results of our word aligner tool in the training corpus used to build the translation table of the statistical MT system Moses (Koehn et al., 2007).
In order to evaluate the alignment quality manually, we used 500 English-Arabic aligned sentences extracted from the MT evaluation MEDAR 1 package and we followed the evaluation framework defined in (Mihalcea and Pedersen, 2003). Table 2 summarizes the results of our word aligner in terms of precision and recall. The first line describes the performance of the word aligner when it does not integrate transliteration and the second line mentions its performance when it uses transliteration. As we can see, the results demonstrate that using transliteration improves both precision and recall of word alignment. These results confirm those obtained by (Sajjad et al., 2003) related to the improvement of alignment quality when integrating transliteration into the GIZA++ word aligner.

Alignment
Precision The unavailability of a reference alignment of a significant size for single and compound words does not allow us to compare our approach with the state-of-the-art work. That's why we decided to study the impact of the use of transliteration in word alignment by integrating the results of our word aligner in the training corpus used to extract the translation model of Moses. The initial training corpus is composed of 75000 pairs of English-Arabic sentences extracted from the MEDAR corpus (2631654 English words and 2344878 Arabic words). We added to this corpus around 28000 pairs of single and compound words corresponding to the results of our word aligner which integrates transliteration applied on 1000 pairs of English-Arabic sentences. We also specified a language model for the target language using a corpus composed of 100000 Arabic sentences (3155516 words). The performance of the statistical machine translation system Moses is evaluated using the BLEU score on a test corpus composed of 500 pairs of sentences. Note that we consider one reference per sentence. The obtained results show that the inclusion in the training corpus of word alignment results integrating transliteration has improved the translation BLEU score from 20.15 to 20.63 (a gain of 0.48 points).
In order to assess statistical significance of the obtained results, we use the paired bootstrap resampling method (Koehn, 2004) which estimates the probability (p-value) that a measured difference in BLEU scores arose by chance by repeatedly (10 times) creating new virtual test sets by drawing sentences with replacement from a given collection of translated sentences. We carry out experiments using this method to compare the translation results without using transliteration and with the use of transliteration. At a 95% confidence interval (CI), the results vary from insignificant (at p > 0.05) to highly significant. The p-value obtained is equal to 0.02 and therefore the improvement achieved by using transliteration is statistically significant.

Conclusion
We presented briefly in this paper a system to transliterate proper names from Arabic to Latin script and we proposed a tool to automatically align word pairs from an English-Arabic parallel corpus. We integrated the transliterated proper names into the cognates matching step and we obtained a gain of 9% on word alignment Fmeasure and a gain of 0.48 points in translation BLEU score. These encouraging results can be improved in a number of ways. First, we plan to affect a weight for each word pair in order to filter the word alignment results and to integrate them directly in the translation table of Moses. We also plan to use, on the one hand, the linguistic analyzer LIMA to lemmatize texts of the bilingual corpus, and on the other hand, factored models and a flexor to generate adequate surface forms from lemmas.