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.

Impact

• General NLP research: We will investigate how to leverage available syntactic resources for families of resource-poor languages.
• Tools: we will create standard tools, i.e. parsers with compatible tokenization and morphological analysis components, for the processing of Arabic (MSA and dialects). These can be used in applications such as dialect translation, information retrieval, information extraction from speech data, dialect transcription, language modeling for ASR, and semantic parsing of Arabic dialects.
• Resources: we will create standards for the transcription of Arabic dialects, as well as grammars and small corpora and lexica.

Opening Day Presentation
Arabic NLP, Tutorial by Nizar Habash
Team Update
Tregex and Tsurgeon, Tutorial by Roger Levy
Closing Day Presentation
Final Report