The demo paper on Automatic Dialect Identification for Arabic has been presented at the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Commenting on the research, Habash notes: “Dialect identification is an important enabling technology that has the potential to support a range of language artificial intelligence applications though better user dialect profiling. For example, dialect-aware machine translation or chatbots can determine whether the correct meaning of the word ماشي /m aa sh i/ is ‘ok’ (in Cairo and Beirut) or ‘no’ (in Sana’a). The confidence and accuracy of the system generally increases with the length of the input sentence, as many short phrases and words can belong to different dialects.”
The interface visualizes the probability distribution into a two-dimensional geographical map space to allow users to easily observe connections and patterns relating to dialectal similarities and differences, and detect aggregations of probabilities of nearby cities that give a sense of regional presence.
For further information about ADIDA, please download the demo paper.