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:  NLP challenges for Arabic dialects 
1. Abstract of Cited Paper (BIB001):  In this paper we describe the construction of a parallel corpus between the standard and a non-standard language variety, specifically standard Austrian German and Viennese dialect. The resulting parallel corpus is used for statistical machine translation (SMT) from the standard to the non-standard variety. The main challenges to our task are data scarcity and the lack of an authoritative orthography. We started with the generation of a base corpus of manually transcribed and translated data from spoken text encoded in a specifically developed orthography. This data is used to train a first phrasebased SMT. To deal with out-of-vocabulary items we exploit the strong proximity between source and target variety with a backoff strategy that uses character-level models. To arrive at the necessary size for a corpus to be used for SMT, we employ a boot-strapping approach. Integrating additional available sources (comparable corpora, such as Wikipedia) necessitates to identify parallel sentences out of substantially differing parallel documents. As an additional task, the spelling of the texts has to be transformed into the above mentioned orthography of the target variety. 
2. Abstract of Cited Paper (BIB002):  We propose a novel language-independent approach for improving machine translation for resource-poor languages by exploiting their similarity to resource-rich ones. More precisely, we improve the translation from a resource-poor source language X1 into a resourcerich language Y given a bi-text containing a limited number of parallel sentences for X1-Y and a larger bi-text for X2-Y for some resource-rich language X2 that is closely related to X1. This is achieved by taking advantage of the opportunities that vocabulary overlap and similarities between the languages X1 and X2 in spelling, word order, and syntax offer: (1) we improve the word alignments for the resource-poor language, (2) we further augment it with additional translation options, and (3) we take care of potential spelling differences through appropriate transliteration. The evaluation for Indonesian → English using Malay and for Spanish → English using Portuguese and pretending Spanish is resource-poor shows an absolute gain of up to 1.35 and 3.37 BLEU points, respectively, which is an improvement over the best rivaling approaches, while using much less additional data. Overall, our method cuts the amount of necessary "real" training data by a factor of 2-5.

Literature Review:
Arabic dialects, despite their large use are under-resourced languages, they lack basic NLP tools. Except some work dedicated to Middle-east dialects (Egyptian dialect mostly), these dialects are not enough studied regarding to NLP area. Most MSA resources and tools are not adapted to them and do not take into account their features. The reader can refer to  which presented a comprehensive survey on Arabic NLP, the work focused on MSA but included many interesting notes on pratical issues 1 The dual form, for example, as well as the feminine plural form used in standard Arabic do not exist in most Arabic dialects. 2 Standard Arabic has a strong case system where most cases are denoted by diacritics. In Arabic dialects, there is no grammatical case, thus generating more syntactic ambiguities compared to MSA. Also, the verbs negation in dialects is more complex than MSA, the circumfix negation is placed around the verb with all its prefixes and suffixed direct and indirect object pronouns. concerning Arabic dialects or to  where authors reported all available tools and resources recently produced for these dialects. One of the main issue of Arabic dialects is the fact that they have no conventional orthographies for writing them. Their large use (because the advent of Internet technologies) produces important volumes of data which are difficult to exploit and require important pre-processing steps. In the area of machine translation, Arabic dialect translation research efforts are still at an early stage. Rule-based approaches are difficult to envisage because of unavailability of dedicated tools for most of these dialects. Indeed, these approaches are being used less and less in MT systems because they are time consuming and require important linguistic resources. Also, MT systems based on these approaches are difficult to maintain, adding new linguistic features involves updating rules or adding new rules. For Arabic dialects, these approaches are more problematic. These dialects are not written and have no strong theoretical linguistic studies that could allow such approach. In addition, these dialects differ from one Arab country to another, even in the same country significant variations exist, any rule-based MT system could not take into account all related features. On the other hand, data-driven approaches are also hard to consider due to the lack of resources like parallel and even monolingual corpora. In the context of statistical machine translation, Arabic dialects lack bi-texts with reasonable sizes that allow building efficient statistical machine translation (SMT) systems readily. It should be noted that this issue does not arise only in the case of Arabic dialects; it concerns also several other under-resourced languages and many research activities focus on machine translation in the context of under-resourced or non-resourced languages. The main idea of these contributions is exploiting the proximity between an under-resourced language and the closest related resourced language (Cantonese⇒Mandarin  , Czech⇒Slovak BIB001 , Turkish⇒Crimean Tatar  , Irish⇒Scottish Gaelic  , Indonesian⇒English using Malay BIB003 and Standard Austrian German⇒Viennese dialect BIB002 ).