Propagate-Selector: Detecting Supporting Sentences for Question Answering via Graph Neural Networks
Table 5: Model performance with different typologies. The connection strategies between nodes for each type are illustrated in figure 4.
['[BOLD] Model', 'dev MAP', 'dev MRR', 'train MAP', 'train MRR']
To reach the best performance, we conduct experiments multiple times by changing the number of hops in the model from 1 to 6 for each case (Type-1 to Type-3). From the experiment, hop-4 is selected as the best-performing hyper-parameter. However, all the model variations undergo performance degradation compared to the original topology (PS-rnn-elmo-s).