A month ago I said[1] I thought the Memory Network approach to these tasks was some of the most important neural network being done atm. Now this comes along and jumps from 33.5% to 87% of the path finding question answering task, and to 97.9% on positional reasoning.
I don't know what the human benchmarks are, but I'd guess the positional reasoning rate is close to human levels.
Also, they didn't report the accuracy on any of the other 18 tasks. That makes me believe that they did pretty badly on those.
> That was my first instinct too. Going back to the dynamic memory networks paper though, it turns out (§4.1) that these are the only two tasks it didn't solve almost-perfectly.
It's worth noting that these tests were introduced by Jason Weston[1] as "A Set of Prerequisite Toy Tasks"[2]
Also, they didn't report the accuracy on any of the other 18 tasks. That makes me believe that they did pretty badly on those.
Weston's work on Memory Networks has "solved" (or at least shown a good way forward) on 16 of those other tasks. These were the two that were furthest away from being solved (See [2], again). The remaining 2 look like they will fall to Memory Networks with some work.
As the second author of the paper, I have to say "Wrong Guess", :), we didn't try the other 18. The story is as follows. We had this idea in early March as a reasoning component of our bigger (and cool) neural system. In early May my student intern (the first author) decided to implement it, but he said "why don't we try this on a couple of hard bAbi tasks on which Memory Net failed？", and then we are here. That said, I don't mean Neural Reasoner can easily solve them without any model tweaking, considering some of them are formulated slightly differently from the two we tried . But more importantly, it was never our goal to nail an artificial data set.
> For simplicity, we focus on the reasoning tasks which can be formulated as classification with predetermined classes.