Machine translation for Arabic dialects (survey) <s> Conclusion <s> We present a novel method for extracting parallel sub-sentential fragments from comparable, non-parallel bilingual corpora. By analyzing potentially similar sentence pairs using a signal processing-inspired approach, we detect which segments of the source sentence are translated into segments in the target sentence, and which are not. This method enables us to extract useful machine translation training data even from very non-parallel corpora, which contain no parallel sentence pairs. We evaluate the quality of the extracted data by showing that it improves the performance of a state-of-the-art statistical machine translation system. <s> BIB001 </s> Machine translation for Arabic dialects (survey) <s> Conclusion <s> The paper presents a novel sentence pair extraction algorithm for comparable data, where a large set of candidate sentence pairs is scored directly at the sentence-level. The sentence-level extraction relies on a very efficient implementation of a simple symmetric scoring function: a computation speed-up by a factor of 30 is reported. On Spanish-English data, the extraction algorithm finds the highest scoring sentence pairs from close to 1 trillion candidate pairs without search errors. Significant improvements in BLEU are reported by including the extracted sentence pairs into the training of a phrase-based SMT (Statistical Machine Translation) system. <s> BIB002 </s> Machine translation for Arabic dialects (survey) <s> Conclusion <s> Mining parallel data from comparable corpora is a promising approach for overcoming the data sparseness in statistical machine translation and other NLP applications. Even if two comparable documents have few or no parallel sentence pairs, there is still potential for parallelism in the sub-sentential level. The ability to detect these phrases creates a valuable resource, especially for low-resource languages. In this paper we explore three phrase alignment approaches to detect parallel phrase pairs embedded in comparable sentences: the standard phrase extraction algorithm, which relies on the Viterbi path; a phrase extraction approach that does not rely on the Viterbi path, but uses only lexical features; and a binary classifier that detects parallel phrase pairs when presented with a large collection of phrase pair candidates. We evaluate the effectiveness of these approaches in detecting alignments for phrase pairs that have a known alignment in comparable sentence pairs. The results show that the Non-Viterbi alignment approach outperforms the other two approaches on F1 measure. <s> BIB003 </s> Machine translation for Arabic dialects (survey) <s> Conclusion <s> Microblogging services such as Twitter have become popular media for real-time usercreated news reporting. Such communication often happens in parallel in different languages, e.g., microblog posts related to the same events of the Arab spring were written in Arabic and in English. The goal of this paper is to exploit this parallelism in order to eliminate the main bottleneck in automatic Twitter translation, namely the lack of bilingual sentence pairs for training SMT systems. We show that translation-based cross-lingual information retrieval can retrieve microblog messages across languages that are similar enough to be used to train a standard phrase-based SMT pipeline. Our method outperforms other approaches to domain adaptation for SMT such as language model adaptation, meta-parameter tuning, or self-translation. <s> BIB004
The above findings draw a picture of machine translation in the context of Arabic dialects. We can observe that dialects emerge as real languages and any NLP tools and resources dedicated to MSA should taking into account these dialects. Machine translation for Arabic dialects is still an immature area of research. There is still a long way to walk. Several important issues need to be solved. The dialects themselves, as they are presented in all the research work are classified by country or by region: Levantine dialect, Egyptian, Algerian, Tunisian, etc. This classification simplifies considerably the real linguistic situation through Arab countries. In fact, each Arab country has multiple varieties of dialects with specific features. MT systems dedicated to dialect have to deal with all these variants. In addition, the wide use of Arabizi in social networks generates new challenges that needs to be addressed also. Another issue has to be taken into account is the code switching, Arab people switch in their conversation between dialect, Arabic and other languages, especially in the Maghreb where people tend to use French, Arabic, dialect and even Berber. This code-switching is a challenge for dialects MT. Also, it should be noted that for Maghreb dialect an important source of OOV words could be the use of French words, handling this issue must take into account this aspect since MSA pivoting or normalizing Maghreb dialect words to MSA could be insufficient. In the same vein, fast evolution of dialects needs to be considered for machine translation. Indeed, everyday new dialectal words appear and are adopted by people spontaneously without any official or academic validation. As regards resources, a way to get parallel data is to use an iterative approach to produce artificial dialectal data from available dialect MT systems by post-editing their output. Another interesting track is to investigate comparable corpora for producing parallel corpora for training machine translation systems. This is already done for natural language such as in : BIB004 , BIB003 for the pair Arabic-English,  for English-German and Arabic-English, BIB001 for Romanian-English, and BIB002 ) for Spanish-English and Portuguese-English. This approach is feasible for Arabic dialects by using social networks which are a rich source containing a huge quantity of data expressed in dialects. But unfortunately, these noisy data require a considerable pre-processing steps such as: dialect identification, morphological analysis with specific tools, cleaning the data by eliminating non-exploitable fragments and writing normalization.