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Title: Adaptive Memory Networks

Paper content:
Real-world Question Answering (QA) tasks consist of thousands of words that often represent many facts and entities.
Existing models based on LSTMs require a large number of parameters to support external memory and do not generalize well for long sequence inputs.
Memory networks attempt to address these limitations by storing information to an external memory module but must examine all inputs in the memory.
Hence, for longer sequence inputs the intermediate memory components proportionally scale in size resulting in poor inference times and high computation costs.
In this paper, we present Adaptive Memory Networks (AMN) that process input question pairs to dynamically construct a network architecture optimized for lower inference times.
During inference, AMN parses input text into entities within different memory slots.
However, distinct from previous approaches, AMN is a dynamic network architecture that creates variable numbers of memory banks weighted by question relevance.
Thus, the decoder can select a variable number of memory banks to construct an answer using fewer banks, creating a runtime trade-off between accuracy and speed.
AMN is enabled by first, a novel bank controller that makes discrete decisions with high accuracy and second, the capabilities of a dynamic framework (such as PyTorch) that allow for dynamic network sizing and efficient variable mini-batching.
In our results, we demonstrate that our model learns to construct a varying number of memory banks based on task complexity and achieves faster inference times for standard bAbI tasks, and modified bAbI tasks.
We achieve state of the art accuracy over these tasks with an average 48% lower entities are examined during inference.
Question Answering (QA) tasks are gaining significance due to their widespread applicability to recent commercial applications such as chatbots, voice assistants and even medical diagnosis BID7 ).
Furthermore, many existing natural language tasks can also be re-phrased as QA tasks.
Providing faster inference times for QA tasks is crucial.
Consumer device based question-answer services have hard timeouts for answering questions.
For example, Amazon Alexa, a popular QA voice assistant, allows developers to extend the QA capabilities by adding new "Skills" as remote services BID0 ).
However, these service APIs are wrapped around hard-timeouts of 8 seconds which includes the time to transliterate the question to text on Amazon's servers and the round-trip transfer time of question and the answer from the remote service, and sending the response back to the device.
Furthermore, developers are encouraged to provide a list of questions ("utterances") apriori at each processing step to assist QA processing BID0 ).Modeling
QA tasks with LSTMs can be computationally expensive which is undesirable especially during inference. Memory networks
, a class of deep networks with explicit addressable memory, have recently been used to achieve state of the art results on many QA tasks. Unlike LSTMs,
where the number of parameters grows exponentially with the size of memory, memory networks are comparably parameter efficient and can learn over longer input sequences. However, they
often require accessing all intermediate memory to answer a question. Furthermore,
using focus of attention over the intermediate state using a list of questions does not address this problem. Soft attention
based models compute a softmax over all states and hard attention models are not differentiable and can be difficult to train over a large state space. Previous work
on improving inference over memory networks has focused on using unsupervised clustering methods to reduce the search space BID2 ; BID19 ). Here, the memory
importance is not learned and the performance of nearest-neighbor style algorithms is often comparable to a softmax operation over memories. To provide faster
inference for long sequence-based inputs, we present Adaptive Memory Networks (AMN), that constructs a memory network on-the-fly based on the input. Like past approaches
to addressing external memory, AMN constructs the memory nodes dynamically. However, distinct from
past approaches, AMN constructs a memory architecture with network properties that are decided dynamically based on the input story. Given a list of possible
questions, our model computes and stores the entities from the input story in a memory bank. The entities represent the
hidden state of each word in the story while a memory bank is a collection of entities that are similar w.r.t the question. As the number of entities
grow, our network learns to construct new memory banks and copies entities that are more relevant towards a single bank. Entities may reside in different
bank depending on their distance from the question. Hence, by limiting the decoding
step to a dynamic number of constructed memory banks, AMN achieves lower inference times. AMN is an end-to-end trained model
with dynamic learned parameters for memory bank creation and movement of entities.Figure 1 demonstrates a simple QA task where AMN constructs two memory banks based on the input. During inference only the entities
in the left bank are considered reducing inference times. To realize its goals, AMN introduces
a novel bank controller that uses reparameterization trick to make discrete decisions with high accuracy while maintaining differentiability. Finally, AMN also models sentence structures
on-the-fly and propagates update information for all entities that allows it to solve all 20 bAbI tasks.
In this paper, we present Adaptive Memory Network that learns to adaptively organize the memory to answer questions with lower inference times.
Unlike NTMs which learn to read and write at individual memory locations, Adaptive Memory Network demonstrates a novel design where the learned memory management is coarse-grained that is easier to train.Through our experiments, we demonstrate that AMN can learn to reason, construct, and sort memory banks based on relevance over the question set.AMN architecture is generic and can be extended to other types of tasks where the input sequence can be separated into different entities.
In the future, we plan to evaluate AMN over such tasks to evaluate AMN generality.
We also plan to experiment with larger scale datasets (beyond bAbI, such as a document with question pairs) that have a large number of entities to further explore scalability.Method Complexity Entnet BID12 We describe our overall algorithm in pseudo-code in this section.
We follow the notation as described in the paper.
DISPLAYFORM0 Algorithm 1 AMN(S, q, a) DISPLAYFORM1 for word w ∈ s do 4: DISPLAYFORM2 end for 6: DISPLAYFORM3 for memory bank m i ∈ M do 8: DISPLAYFORM4 n mi ← SGRU(D, n mi ) We compare the computations costs during the decode operation during inference for solving the extended bAbi task.
We compute the overheads for AMN Entnet BID12 ) and GGT-NN.
TAB2 gives the decode comparisons between AMN, Entnet and GGT-NN.
Here, |V | represents to the total number of entities for all networks.
GGT-NN can dynamically create nodes and k k is hyper parameter the new nodes created for S sentences in input story.
α is the percent of entities stored in the final bank w.r.t to the total entities for AMN.We compare the wall clock execution times for three tasks within bAbI for 1000 examples/task.
We compare the wall-clock times for three tasks.
We compare the inference times of considering all banks (and entities) versus the just looking at the passing banks as required by AMN.
We find that AMN requires fewer banks and as a consequence fewer entities and saves inference times.
In this section, we understand memory bank behavior of AMN.
Figure 3 shows the memory banks and the entity creation for a single story example, for some of the tasks from bAbI.
Depending upon the task, and distance from the question AMN creates variable number of memory banks.
The heatmap demonstrates how entities are copied across memory banks.
Grey blocks indicate absence of those banks.Under review as a conference paper at ICLR 2018 Figure 4 shows how propagation happens after every time step.
The nodes represent entities corresponding to words in a sentence.
As sentences are processed word by word, a directed graph is drawn progressively from w 0 ...w i ...w N .
If sentence l k 's path contains nodes already in the current directed graph, l k will include said nodes in the its path. After l k is added to A, the model propagates the new update hidden state information a i among all node states using a GRU. a i for each node i is equal to the sum of the incoming edges' node hidden states.
Additionally, we add a particular emphasis on l k to simulate recency.
At face value, one propagation step of A will only have a reachability of its immediate neighbor, so to reach all nodes, A is raised to a consecutive power r to reach and update each intermediate node.
r can be either the longest path in A or a set parameter.

assistant: text: Memory networks with faster inference