UPB at SemEval-2021 Task 8: Extracting Semantic Information on Measurements as Multi-Turn Question Answering
This work addresses the extraction of measurements and counts from scientific discourses, which is important for analyzing scientific texts, but it is incremental as it applies an existing method (multi-turn QA) to a new dataset in a competition setting.
The paper tackled the problem of extracting semantic information on measurements from scientific texts by participating in the SemEval-2021 MeasEval task, using a multi-turn question answering approach to address five subtasks, with the best model achieving an overlapping F1-score of 36.91% on the test set.
Extracting semantic information on measurements and counts is an important topic in terms of analyzing scientific discourses. The 8th task of SemEval-2021: Counts and Measurements (MeasEval) aimed to boost research in this direction by providing a new dataset on which participants train their models 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 identified quantities and their value modifier classification, (3) span identification for measured entities and measured properties, (4) qualifier span identification, and (5) relation extraction between the identified quantities, measured entities, measured properties, and qualifiers. We approached these challenges by first identifying the quantities, extracting their units of measurement, classifying them with corresponding modifiers, and afterwards using them to jointly solve the last three subtasks in a multi-turn question answering manner. Our best performing model obtained an overlapping F1-score of 36.91% on the test set.