EQuANt (Enhanced Question Answer Network)
(b) SQuAD 1.1 dev set results.
['Name', 'No. of Params', 'Training Iterations', 'EM', 'F1']
The quoted number of training iterations for other models in tables These EM/F1 scores are 9.98/6.853 lower than the corresponding results for the full QANet architecture Yu et al. , implying that our choice to employ a lightweight architecture has a noticeable impact on performance. As laid out in this blog post by Sebastian Ruder, multi-task learning has recently been successfully applied to numerous NLP tasks. We therefore decided to measure the performance of EQuANt 3, trained on the two tasks of question answering and answerability prediction, at question answering alone by evaluating its EM and F1 scores on SQuAD 1 by providing EQuANt 3 with the ground truth answerability of true for each SQuAD1 question.