SOURCE-LINKED INTELLIGENCE

Natural Language Understanding for non-standard languages and dialects

CORDIS · observation · Publication date unknown

ity in inputs, and interactive learning which integrates human uncertainty in labels. This will reduce the need for data and enable better adaptation and generalization. Advances in salient areas of deep learning research now make it possible to tackle this challenge. DIALECT’s objectives are to devise a) new algorithms and insights to address extremely scarce data setups and biased labels; b) novel representations which integrate auxiliary sources of information such as complement text data with speech; and c) new datasets with conversational data in its most natural form. By integrating dialectal variation into models able to learn from scarce data and biased labels, the foundations will be established for fairer and more accurate NLU to break down language and literary barriers. I am privileged to carry out this integration as I have contributed to research in top venues on both cross-lingual learning and learning from biased labels. low-resource natural language processing (NLP), NLP for non-standard language, distributed representations for multilingual text

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recordType

award

status

SIGNED

region

EU

value

1997815

unit

EUR