Empirical Study on Deep Learning Models for QA
Table 1: AI-complete 20 tasks results.
['[EMPTY]', '(i) Memory Network a', '(i) Memory Network b', '(i) Memory Network c', '(ii) Support fact only d', '(ii) Support fact only e', '(iii) Sup. fact highlighted f', '(iii) Sup. fact highlighted g', '(iv) Combination h', '(v) No support fact i', '(v) No support fact j']
So first we want to learn how different MemNN components contribute to it. We divide it into two virtual steps as the 2-step QA framework. We believe searching supporting facts (MemNN-S) is very important to MemNN while the MemNN response module (MemNN-R) has more room to improve, because it does not have any attention mechanism to focus on words in retrieved facts. We train MemNN-S by using complete passage as input and supporting facts as prediction and train MemNN-R by using true supporting facts as input and final answer as prediction.