Propagate-Selector: Detecting Supporting Sentences for Question Answering via Graph Neural Networks
Table 6: Model performance with the different method for computing node representation.
['[BOLD] Model', 'dev MAP', 'dev MRR', 'train MAP', 'train MRR']
In all cases, the RNN encoding skims (-rnn) performs better than that of the average pooling (-avg). Interestingly, average pooling with ELMo representation ( PS-avg-elmo-s) performs worse than in the GloVe representation (PS-avg-glove) case. From this result, we find that averaging ELMo does not produce proper node representations. For the PS-rnn-bert case, we do not fine-tune the BERT model and only use its computing word representation. We expect there exists a possibility to enhance model performance by fine-tuning the BERT with the end-to-end training process.