Abstract

Developments in the field of Computational Linguistics produce ever higher-quality technology for processing and disseminating language information. Given the ubiquity of tablets and smart phones, such technology can be of increasing social benefit. At the user level, applications might provide personalised responses, for instance, providing highly targeted feedback to language learners. At a societal level, applications might monitor the development of a humanitarian crisis by automatically processing social media messages; or gather intelligence quickly from public news and other social media sources. Global deployment of such applications requires language processing technology that can accurately model the specific language of an individual; small group of non-standard language users; or so-called low-resource languages. This is at odds with recent improvements in NLP technology which are heavily reliant on extremely large amounts of language data to train data-hungry statistical learning algorithms. This talk will discuss the extent of this problem and possible solutions.