Towards Neural Network-based Reasoning
arxiv.org
arxiv.org
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.
> For simplicity, we focus on the reasoning tasks which can be formulated as classification with predetermined classes.
> 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.
Whilst I love the theory behind the bAbi dataset, there are issues with the data itself that still need to be fixed, particularly that there is duplication in the datasets[2]. This duplication leads to less unique data than expected and even overlaps between training and testing data. The problem gets worse as you get to the 10k subsets[3]. Most worryingly, authors who I've spoken to aren't aware of these issues.
As an example, QA17 (Positional Reasoning) which is one of the focus areas of this paper has two major issues. First, instead of 10,000 / 1,000 unique train / test questions, duplication means it only has 5,812 / 632. Second, 15% of the exact training questions in the 10k subset are repeated in the test set.
I've contacted the creators of the dataset at Facebook but they don't seem to think the issue is that extreme. The authors of papers using the bAbi dataset are also not noting which version of the data they've used, which is problematic if there are future updates...
[1]: https://github.com/fchollet/keras/blob/master/examples/babi_...
[2]: http://smerity.com/articles/2015/keras_qa.html#dataset-issue...
I imagine a simplified architecture of RNN->Highway CNN->Answerer would achieve better results while being more homogeneous.
Task I: path finding
1.The office is east of the hallway.
2.The kitchen is north of the office.
3.The garden is west of the bedroom.
4.The office is west of the garden.
5.The bathroom is north of the garden.
How do you go from the kitchen to the garden?
-> south, east, relies on 2 and 4
How do you go from the office to the bathroom?
-> east, north, relies on 4 and 5
Task II: positional reasoning
1.The triangle is above the pink rectangle.
2.The blue square is to the left of the triangle.
Is the pink rectangle to the right of the blue square?
-> Yes, relies on 1 and 2
Is the blue square below the pink rectangle?
-> No, relies on 1 and 2For university I'm currently writing something similar (with Prolog), its basically a pipeline that goes something like this: (1) natural language -> (2) syntax tree -> (3) annotate syntax tree with logical (in my case, lambda) terms that represent the semantics -> (4) use semantics to query knowledge base.
(2) requires you to write a grammar, which is a lot of work, even for a very limited subset of your target language. (3) is even harder, for every term and grammatical construction that you want to be able to parse, you have to write rules that translate the semantics into something the computer understands. And (4) is quite time-consuming as well, since you have to translate your knowledge into something that the computer can understand (in my case, logical terms).
From what I can understand, this system allows you to skip (2), (3) and (4) i.e. you just feed the system your facts and questions and it will perform the reasoning by itself, no more hand-written rules. Basically, you feed it only natural language (easy to do, even for untrained people!) and it does the rest on its own. Which is a huge timesaver, obviously :-)
Do you know how they manage to solve arbitrary-length paths using a finite-depth neural network? Do they use an iterative approach?
And what if I want the system to learn relations inside an arbitrarily large corpus of text, does this mean that the neural network also needs to be of increasing complexity?
This is not overall an RNN; they only use an RNN layer in the initial layer to parse the questions & facts (pg3), then it gets fed into a regular feedforward deep network. I think any specific implementation of this would have a limit; for example, figure 2 implies that each 'fact' gets its own stack of DNNs, so for k facts you need k stacks of DNNs. And each stack can only do so much computation based on how many layers there are, so they can only solve certain length problems before forgetting/running out of time.
So I would guess the answers to your questions are 'it doesn't, 'no', and 'yes'. (None of which is necessarily bad. It's not like humans can solve arbitrary-length problems in our heads either.)