HAS-QA: Hierarchical Answer Spans Model for Open-domain Question Answering
Table 3: Results on QuasarT with different types of aggregation functions (K1=3,K2=1).
['Model', 'EM', 'F1']
They take advantages of using multiple answer spans for training and improve about 6% - 10% in EM comparing to the HEAD operation. The performance of MAX operation is a little better than the SUM operation. The failure of RAND operation, mainly comes down to the conflicting training samples. Therefore, simple way to make use of multiple answer spans may not improve the performance.