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C. Biemann (Univ. Hamburg): Adaptive Interpretable Language Technology

Institutional tag: 

Automatic natural language understanding enables natural communication with computers and computer-assisted access to the content of large document collections. While classical approaches to artificial intelligence anticipate all possible situations and interactions in form of a fully specified dialogue model or ontology, they are hard to adapt to new domains and do not cope well with language change.
In this talk, I will motivate an adaptive, purely data-driven approach to natural language processing. Illustrated by recent research prototypes, three stages of data-driven adaptation will be illustrated: feature/resource induction, induction of processing components and continuous data-driven learning with the human in the loop. Finally, I will discuss current research and future directions regarding the integration of symbolic and statistical knowledge, interpretability of language processing components as well as advanced forms of information access. The future of language technology will yield more adaptive, more user-centric technologies than the one-size-fits-all solutions currently being popularized in AI applications such as digital assistants.

Dates: 
Monday, May 28, 2018 - 14:00 to 15:00
Location: 
Inria B21
Speaker(s): 
Chris Biemann
Affiliation(s): 
University of Hamburg