Question-Answering with Grammatically-Interpretable Representations
Table 1: Performance of the proposed TPRN model compared to BiDAF proposed in [Seo et al.2016]
['Single Model', 'EM(dev)', 'F1(dev)', 'EM(test)', 'F1(test)']
We compared the performance of single models. In the TPRN model tested here for performance comparison purposes, we set the number of symbols and roles to 600 and 100 respectively and the embedding size of symbols and roles to 15 and 10. Each experiment for the TPRN model took about 13 hours on a single Tesla P100 GPU. Overall, the proposed TPRN gives results comparable to those of the state-of-the-art BiDAF model. Moreover, as we will elaborate in the following sections, our model offers considerable interpretability thanks to the structure built into TPRs.