Problem Definition: The proposed project will tackle the problem of parsing Arabic dialects. Parsing is an important component in many advanced NLP systems, and has also been shown to be useful for language modeling for ASR. As is well known, Arabic exhibits diglossia, i.e., the coexistence of two forms of language, a high variety with standard orthography and sociopolitical clout which is not natively spoken by anyone (Modern Standard Arabic, MSA) and low varieties that are primarily spoken and lack writing standards (Arabic dialects). The dialects and MSA form a continuum of variation at the lexical, phonological, morphological, and syntactic levels.
There are important resources currently available for MSA with much on-going NLP work; for example, there are several syntactic and semantic parsers for MSA. However, Arabic dialect resources and NLP research are still at an infancy stage. There are linguistic studies of Arabic dialectal syntax but there is no language engineering work (such as computational grammars). There are no parallel written corpora between any of the dialects and any other language, including MSA. Thus, most of the techniques developed for parsing that exploit supervised (in the canonical sense) machine learning do not apply, since there is no sufficient annotated data to learn from. We would like to leverage existing resources and tools for MSA in order to parse Arabic dialects using both symbolic techniques and machine learning approaches.
|David Chiang||University of Maryland|
|Mona Diab||Columbia University|
|Nizar Habash||Columbia University|
|Rebecca Hwa||University of Pittsburgh|
|Owen Rambow||Columbia University|
|Khalil Sima'an||University of Amsterdam|
|Roger Levy||Stanford University|
|Carol Nichols||University of Pittsburgh|
|Vincent Lacey||Georgia Tech|
|Safiullah Shareef||Johns Hopkins University|