You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Machine translation for Arabic dialects (survey) 
Chapter Title:  Translating between MSA and Arabic dialects 
1. Abstract of Cited Paper (BIB001):  We present MAGEAD, a morphological analyzer and generator for the Arabic language family. Our work is novel in that it explicitly addresses the need for processing the morphology of the dialects. MAGEAD performs an on-line analysis to or generation from a root+pattern+features representation, it has separate phonological and orthographic representations, and it allows for combining morphemes from different dialects. We present a detailed evaluation of MAGEAD. 
2. Abstract of Cited Paper (BIB002):  Recently the rate of written colloquial text has increased dramatically. It is being used as a medium of expressing ideas especially across the WWW, usually in the form of blogs and partially colloquial articles. Most of these written colloquial has been in the Egyptian colloquial dialect, which is considered the most widely dialect understood and used throughout the Arab world. Modern Standard Arabic is the official Arabic language taught and understood all over the Arab world. Diacritics play a key role in disambiguating Arabic text. The reader is expected to infer or predict vowels from the context of the sentence. Inferring the full form of the Arabic word is also useful when developing Arabic natural language processing tools and applications. In this paper, we introduce a generic method for converting a written Egyptian colloquial sentence into its corresponding diacritized Modern Standard Arabic sentence which could easily be extended to be applied to other dialects of Arabic. In spite of the non-availability of linguistic Arabic resources for this task, we have developed techniques for lexical acquisition of colloquial words which are used for transferring written Egyptian Arabic into Modern Standard Arabic. We successfully used Support Vector Machine approach for the diacritization (aka vocalization or vowelling) of Arabic text. 
3. Abstract of Cited Paper (BIB003):  This paper is about improving the quality of Arabic-English statistical machine translation (SMT) on dialectal Arabic text using morphological knowledge. We present a light-weight rule-based approach to producing Modern Standard Arabic (MSA) paraphrases of dialectal Arabic out-of-vocabulary (OOV) words and low frequency words. Our approach extends an existing MSA analyzer with a small number of morphological clitics, and uses transfer rules to generate paraphrase lattices that are input to a state-of-the-art phrase-based SMT system. This approach improves BLEU scores on a blind test set by 0.56 absolute BLEU (or 1.5% relative). A manual error analysis of translated dialectal words shows that our system produces correct translations in 74% of the time for OOVs and 60% of the time for low frequency words. 
4. Abstract of Cited Paper (BIB004):  We present a method for generating Colloquial Egyptian Arabic (CEA) from morphologically disambiguated Modern Standard Arabic (MSA). When used in POS tagging, this process improves the accuracy from 73.24% to 86.84% on unseen CEA text, and reduces the percentage of out-of-vocabulary words from 28.98% to 16.66%. The process holds promise for any NLP task targeting the dialectal varieties of Arabic; e.g., this approach may provide a cheap way to leverage MSA data and morphological resources to create resources for colloquial Arabic to English machine translation. It can also considerably speed up the annotation of Arabic dialects. 
5. Abstract of Cited Paper (BIB005):  The development of natural language processing tools for dialects faces the severe problem of lack of resources. In cases of diglossia, as in Arabic, one variant, Modern Standard Arabic (MSA), has many resources that can be used to build natural language processing tools. Whereas other variants, Arabic dialects, are resource poor. Taking advantage of the closeness of MSA and its dialects, one way to solve the problem of limited resources, consists in performing a translation of the dialect into MSA in order to use the tools developed for MSA. We describe in this paper an architecture for such a translation and we evaluate it on Tunisian Arabic verbs. Our approach relies on modeling the translation process over the deep morphological representations of roots and patterns, commonly used to model Semitic morphology. We compare different techniques for how to perform the cross-lingual mapping. Our evaluation demonstrates that the use of a decent coverage root+pattern lexicon of Tunisian and MSA with a backoff that assumes independence of mapping roots and patterns is optimal in reducing overall ambiguity and increasing recall. 
6. Abstract of Cited Paper (BIB006):  Modern Standard Arabic (MSA) has a wealth of natural language processing (NLP) tools and resources. In comparison, resources for dialectal Arabic (DA), the unstandardized spoken varieties of Arabic, are still lacking. We present ELISSA, a machine translation (MT) system for DA to MSA. ELISSA employs a rule-based approach that relies on morphological analysis, transfer rules and dictionaries in addition to language models to produce MSA paraphrases of DA sentences. ELISSA can be employed as a general preprocessor for DA when using MSA NLP tools. A manual error analysis of ELISSA’s output shows that it produces correct MSA translations over 93% of the time. Using ELISSA to produce MSA versions of DA sentences as part of an MSA-pivoting DA-to-English MT solution, improves BLEU scores on multiple blind test sets between 0.6% and 1.4%. 
7. Abstract of Cited Paper (BIB007):  Today, several tools exist for automatic processing of standard Arabic. In comparison, resources and tools for Arabic dialects especially Moroccan dialect are still lacking. Given the proximity of the Moroccan dialect with standard Arabic, a way is to provide a translation of the first to the second by combining a rule-based approach and a statistical approach, using tools designed for Arabic standard and adapting these tools to Moroccan dialect. We describe in this paper an architecture for such a translation. 
8. Abstract of Cited Paper (BIB008):  Modern Standard Arabic (MSA) is the formal language in most Arabic countries. Arabic Dialects (AD) or daily language differs from MSA especially in social media communication. However, most Arabic social media texts have mixed forms and many variations especially between MSA and AD. This paper aims to bridge the gap between MSA and AD by providing a framework for the translation of texts of social media. More precisely, this paper focuses on the Tunisian Dialect of Arabic (TAD) with an application on automatic machine translation for a social media text into MSA and any other target language. Linguistic tools such as a bilingual TAD-MSA lexicon and a set of grammatical mapping rules are collaboratively constructed and exploited in addition to a language model to produce MSA sentences of Tunisian dialectal sentences. This work is a first-step towards collaboratively constructed semantic and lexical resources for Arabic Social Media within the ASMAT (Arabic Social Media Analysis Tools) project.

Literature Review:
Elissa  ) is a rule-based machine translation system from Arabic dialects to MSA. It handles Levantine, Egyptian, Iraqi, and to a lesser degree Gulf Arabic. After identifying dialectal words in a source sentence, Elissa produces MSA paraphrases using ADAM BIB004 dialectal morphological analyzer, morphological transfer rules and dialect-MSA dictionaries. These paraphrases are used to form an MSA lattice that passes through a language model (LM) for n-best decoding and then selects the best MSA translations. In this paper, no evaluation has been provided. 3 Mohamed et al. (2012) presented a rule-based approach to produce Colloquial Egyptian Arabic (CEA) from modern standard Arabic, they provide an application case to the Part-Of-Speech (POS) tagging task for which the accuracy has been improved from 73.24% to 86.84% on unseen CEA text, and the percentage of Out-Of-Vocabulary (OOV) words decreased from 28.98% to 16.66%. Al-Gaphari and Al-Yadoumi (2012) used a rule-based approach to convert Sanaani dialect to MSA. Their system reached 77.32% of accuracy when tested on a Sanani corpus of 9386 words. BIB006 presented a translation system between MSA and Tunisian dialect verbal forms. The work is based on deep morphological representations of roots and patterns which is an important feature of Arabic and its variants (dialects). The approach is similar to that used in BIB005 ,  and BIB007 but is characterized by a deep morphological representation based on MAGEAD BIB002 (morphological analyzer and generator for the Arabic dialects). The system translates in both directions (MSA to Tunisian dialect and vice versa). It reached a recall of 84% from dialect to MSA and 80% in the opposite side. For translating Moroccan dialect to MSA, a rule-based approach relying on a language model was used in BIB008 . The system is based on a morphological analysis with Alkhalil morphological analyzer  adapted and extended with Moroccan dialects affixes and a bilingual dictionary (built from television productions scenarios and data collected from the web). After an identification step which separates dialectal data from MSA, the text is analyzed and segmented into annotated dialect units. These outputs are linked into one or more MSA corresponding units by using the bilingual dictionary. In the generation step, MSA phrases are produced then passed to a language model to produce the most fluent MSA sentences (no evaluation was given for this work). BIB009 provided a framework for translating Tunisian dialect text of social media into MSA. The work is based on a bilingual lexicon created for this context. It adopts a set of grammatical mapping rules with a disambiguation step which relies on a language modeling of MSA for the selection of the best translation phrases. It should be noted that the translation system is word-based. It performs a BLEU BIB001 score of 14.32 on a test set of 50 Tunisian dialect sentences (the reference was made by hand).  presented PADIC a multi-dialect Arabic corpus that includes MSA, Maghrebi dialects (Algerian and Tunisian) and Levantine dialects (Palestinian and Syrian). Unlike other contributions, several experiments were performed on different SMT systems between all pairs of languages (MSA and dialects). The authors analyzed the impact of the language model on machine translation by varying the smoothing techniques and by interpolating it with a larger one. The best results of translation were achieved between the dialects of Algeria which is not a surprising result since they share a large part of the vocabulary. It was also shown that the BIB003 Egyptian MSA  Levantine, Egyptian, MSA Iraqi, Gulf Arabic BIB005 MSA Egyptian (Al-Gaphari and Al-Yadoumi, 2012) Sanaani (Yemenite) MSA BIB006 Tunisian MSA MSA Tunisian BIB008 Moroccan MSA BIB009 Tunisian MSA  Algerian, Tunisian, MSA Syrian and Palestinian MSA Algerian, Tunisian, Syrian and Palestinian performance of machine translation between Palestinian and Syrian was relatively high because of the closeness of the two dialects. Concerning MSA, the best results of machine translation have been achieved with Palestinian dialect. In Table 1 , we summarize all the work cited above in terms of concerned dialects and translation direction.