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Extracting semantic information on measurements and counts is an importanttopic in terms of analyzing scientific discourses. The 8th task ofSemEval-2021: Counts and Measurements (MeasEval) aimed to boost research inthis direction by providing a new dataset on which participants train theirmodels to extract meaningful information on measurements from scientific texts.The competition is composed of five subtasks that build on top of each other:(1) quantity span identification, (2) unit extraction from the identifiedquantities and their value modifier classification, (3) span identification formeasured entities and measured properties, (4) qualifier span identification,and (5) relation extraction between the identified quantities, measuredentities, measured properties, and qualifiers. We approached these challengesby first identifying the quantities, extracting their units of measurement,classifying them with corresponding modifiers, and afterwards using them tojointly solve the last three subtasks in a multi-turn question answeringmanner. Our best performing model obtained an overlapping F1-score of 36.91% onthe test set.

UPB at SemEval-2021 Task 8: Extracting Semantic Information on Measurements as Multi-Turn Question Answering