Link to original paper: https://arxiv.org/pdf/1410.3916v11.pdf
The paper is quite influential publication in the subfield of Question Answering. Authored by Jason Weston, Sumit Chopra, and Antoine Bordes, this paper introduced a class of models called memory networks.
The intuitive idea is that in order to accurately answer a question regarding a piece of text, you need to somehow store the initial information given to you. If I were to ask you the question “What does RNN stand for”, (assuming you’ve read this post fully J) you’ll be able to give me an answer because the information you absorbed by reading the first part of this post was stored somewhere in your memory. You just had to take a few seconds to locate that info and articulate it in words. Now, I have no clue how the brain is able to do that, but the idea of having a storage place for this information still remains.
The memory network described in the paper is unique because it has an associative memory that it is able to read and write to. It’s interesting to note that we don’t have this type of memory with CNNs or with Q-Networks (for reinforcement learning) or with traditional neural nets. This is in part because the task of question answering relies so heavily upon being able to model or keep track of long-term dependencies, such as keeping track of the characters in a story or a timeline of events. With CNNs and Q-Networks, “memory” is sort of built into the weights of the network as it learns different filters or mappings from states to actions. At first look, RNNs and LSTMs could be used, but these typically aren’t able to remember or memorize inputs from the past (which in question answering is quite critical).
Okay, so now let’s look at how this network processes the initial text it is given. Like with most machine learning algorithms, the first step is to convert the input into a feature representation. This could entail using word vectors, part of speech labeling, parsing, etc. It’s really just up to the programmer.
The next step would be taking the feature representation I(x) and allowing our memory m to be updated to reflect the new input x we’ve received.
You can consider the memory m to be a sort of an array that is made up of individual memories mi. Each of these individual memories mi can be a function of the memory m as a whole, the feature representation I(x), and/or itself. This function G can be as simple as just storing the whole representation I(x) in the individual memory unit mi. You can modify this function G to update past memories based on new input. The 3rd and 4th steps involve reading from memory, based on the question, to get a feature representation o, and then decoding it to output a final answer r.
The function R could be an RNN that is used to convert the feature representations from memory into a readable and accurate answer to the question.
Now, let’s look at step 3 a little closer. We want this O module to output a feature representation that would best match a possible answer to our given question x. Now this question is going to be compared to every single individual memory unit and is going to be “scored” based on how well the memory unit supports the question.
We take the argmax of the scoring function to find the output representation that best supports the question (You can also take multiple of the highest scoring units, doesn’t have to be limited to 1). The scoring function is one that computes the matrix product between different embeddings of the question and the chosen memory unit[s] (check paper for more details). You can think of this as when you multiply the word vectors of two words in order to find their similarity. This output representation o is then fed into an RNN or LSTM or another scoring function which will output a readable answer.
This network is trained in a supervised manner where training data includes the original text, the question, supporting sentences, and the ground truth answer. Here is the objective function.
For those interested, these are some papers that built off of this memory network approach