Domain&Social Bias in NLP: Case Study in Language Identification

•••► Topic: Domain and Social Bias in NLP: A Case Study in Language Identification •••► Speaker: Tim Baldwin, Professor of Computer Science, University of Melbourne •••► Who will be interested: People interested in solving natural language processing tasks with learning methods. Background in machine learning, statistical modeling, and basic NLP is required. •••► Where: Coworking Platforma Leonardo •••► When: Wednesday, September 13th, 7 PM •••► Language: English •••► Tickets and Registration: https://gram.ly/FfxC "Two forms of bias that are commonly associated with natural language processing (NLP) tasks are domain bias (implicit bias towards documents from a particular domain, with lower performance over other document types) and social bias (implicit bias towards documents authored by particular types of individuals, with lower performance over documents authored by other types of individuals). In this talk, I will discuss the importance of debiasing NLP models across these dimensions, and strategies that can be employed to achieve this. I will focus the talk on the task of language identification (i.e., identifying the language(s) a written document is authored in)."