Ask Me Anything:Dynamic Memory Networks for Natural Language Processing
Table 4: Effectiveness of episodic memory module across tasks. Each row shows the final accuracy in term of percentages with a different maximum limit for the number of passes the episodic memory module can take. Note that for the 0-pass DMN, the network essential reduces to the output of the attention module.
['20cmMax passes', '20cmtask 3', '[EMPTY]', '[EMPTY]', '[EMPTY]']
We note that for several of the hard reasoning tasks, multiple passes over the inputs are crucial to achieving high performance. For sentiment the differences are smaller. However, two passes outperform a single pass or zero passes. In the latter case, there is no episodic memory at all and outputs are passed directly from the input module to the answer module. We note that, especially complicated examples are more often correctly classified with 2 passes but many examples in sentiment contain only simple sentiment words and no negation or misleading expressions. Hence the need to have a complicated architecture for them is small. The same is true for POS tagging. Here, differences in accuracy are less than 0.1 between different numbers of passes.