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Towards next-generation scientific computing tools for diversity-aware language science and technology

Diversity-aware language technology for conversational data

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Most methods in NLP and linguistics are geared towards dealing with text, not talk. Our research aims to change this by demonstrating the importance of linguistically diverse conversational data (audio+annotations). Our central question is: how can we use computational tools to make the language sciences conversation-ready? This is the next frontier for enabling quantitative approaches to conversational structure and for creating diversity-aware language technology. To get there, we combine methods from comparative linguistics, computational modelling and data science. Our key purposes are to enable broad curation, rapid exploration and rich visualization, as showcased in our 2022 ACL, LREC and Interspeech papers.

Participating organisations

Radboud University Nijmegen
Netherlands eScience Center
Social Sciences & Humanities
Social Sciences & Humanities

Team

MD
Mark Dingemanse
AL
Andreas Liesenfeld
Barbara Vreede
Barbara Vreede
eScience Research Engineer
Netherlands eScience Center
Eva Viviani
Eva Viviani
eScience Research Engineer
Netherlands eScience Center
Pablo Rodríguez-Sánchez
Pablo Rodríguez-Sánchez
Lead RSE
Netherlands eScience Center
Jisk Attema
Programme Manager
Netherlands eScience Center

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