The Combination of BERT and Data Oversampling for Relation Set Prediction

Abstract

In this paper, we engage the Task 2 of the SMART Task 2021 challenge in predicting relations used to identify the correct answer of a given question. This is a subtask of Knowledge Base Question Answering (KBQA) and offers valuable insights for the development of KBQA systems. We introduce our method, combining BERT and data oversampling with text replacements of linked terms to Wikidata and dependent noun phrases, in predicting answer relations in two datasets. For the DBpedia dataset, we obtain F1 of 83.15%, precision of 83.68%, and recall of 82.95%. Meanwhile, for the Wikidata dataset we achieved F1 of 60.70%, precision of 61.63%, and recall of 61.10%.