Descrizione del progetto
Comprensione del linguaggio naturale per lingue e dialetti non standard
Quando i modelli e gli algoritmi linguistici dell’intelligenza artificiale elaborano i numeri di enormi insiemi di dati, sono soggetti a pregiudizi semplicemente perché la diversità linguistica è rappresentata in modo inadeguato. Questa esclusione riguarda milioni di persone che conversano in dialetti o lingue insolite, precludendo loro le tecnologie emergenti del futuro. Il progetto DIALECT, finanziato dall’UE, creerà algoritmi che agevolano livelli elevati di variazione dell’input per consentire l’integrazione di diversi dialetti nella tecnologia linguistica. Inoltre, amplierà le etichette di verità (cioè le istruzioni del computer utilizzate per verificare l’accuratezza nel mondo reale) nell’apprendimento interattivo, includendo elementi di incertezza umana. Il risultato sarà meno impegnativo in termini di dati e consentirà un’elaborazione linguistica più equa e accurata.
Obiettivo
Dialects are ubiquitous and for many speakers are part of everyday life. They carry important social and communicative functions. Yet, dialects and non-standard languages in general are a blind spot in research on Natural Language Understanding (NLU). Despite recent breakthroughs, NLU still fails to take linguistic diversity into account. This lack of modeling language variation results in biased language models with high error rates on dialect data. This failure excludes millions of speakers today and prevents the development of future technology that can adapt to such users.
To account for linguistic diversity, a paradigm shift is needed: Away from data-hungry algorithms with passive learning from large data and single ground truth labels, which are known to be biased. To go past current learning practices, the key is to tackle variation at both ends: in input data and label bias. With DIALECT, I propose such an integrated approach, to devise algorithms which aid transfer from rich variability 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.
Campo scientifico
Parole chiave
Programma(i)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Argomento(i)
Meccanismo di finanziamento
HORIZON-ERC - HORIZON ERC GrantsIstituzione ospitante
80539 MUNCHEN
Germania