Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Long short-term memory networks (LSTMs) were introduced to combat vanishing gradients in simple recurrent neural networks (S-RNNs) by augmenting them with additive recurrent connections controlled by gates.
We present an alternate view to explain the success of LSTMs: the gates themselves are powerful recurrent models that provide more representational power than previously appreciated.
We do this by showing that the LSTM's gates can be decoupled from the embedded S-RNN, producing a restricted class of RNNs where the main recurrence computes an element-wise weighted sum of context-independent functions of the inputs.
Experiments on a range of challenging NLP problems demonstrate that the simplified gate-based models work substantially better than S-RNNs, and often just as well as the original LSTMs, strongly suggesting that the gates are doing much more in practice than just alleviating vanishing gradients.
Long short-term memory networks (LSTM) BID17 have become the de-facto recurrent neural network (RNN) for learning representations of sequences in many research areas, including natural language processing (NLP).
Like simple recurrent neural networks (SRNNs) BID11 , LSTMs are able to learn non-linear functions of arbitrary-length input sequences.
However, they also introduce an additional memory cell to mitigate the vanishing gradient problem BID16 BID4 .
This memory is controlled by a mechanism of gates, whose additive connections allow long-distance dependencies to be learned more easily during backpropagation.
While this view is mathematically accurate, in this paper we argue that it does not provide a complete picture of why LSTMs work in practice.We present an alternate view to explain the success of LSTMs: the gates themselves are powerful recurrent models that provide more representational power than previously appreciated.
To demonstrate this, we first show that LSTMs can be seen as a combination of two recurrent models: (1) an S-RNN, and (2) an element-wise weighted sum of the S-RNN's outputs over time, which is implicitly computed by the gates.
We hypothesize that, for many practical NLP problems, the weighted sum serves as the main modeling component.
The S-RNN, while theoretically expressive, is in practice only a minor contributor that clouds the mathematical clarity of the model.
By replacing the S-RNN with a context-independent function of the input, we arrive at a much more restricted class of RNNs, where the main recurrence is via the element-wise weighted sums that the gates are computing.We test our hypothesis on NLP problems, where LSTMs are wildly popular at least in part due to their ability to model crucial language phenomena such as word order BID0 , syntactic structure BID23 , and even long-range semantic dependencies BID15 .
We consider four challenging tasks: language modeling, question answering, dependency parsing, and machine translation.
Experiments show that while removing the gates from an LSTM can severely hurt performance, replacing the S-RNN with a simple linear transformation of the input results in minimal or no loss in model performance.
We further show that in many cases, LSTMs can be further simplified by removing the output gate, arriving at an even more transparent architecture, where the output is a context-independent function of the weighted sum.
Together, these results suggest that the gates' ability to compute an element-wise weighted sum, rather than the non-linear transition dynamics of S-RNNs, are the driving force behind LSTM's success.
In the above experiments, we show three major ablations of the LSTM.
In the S-RNN experiments (LSTM -GATES), we ablate the memory cell and the output layer.
In the LSTM -S-RNN and LSTM -S-RNN -OUT experiments, we ablate the S-RNN.
As consistent with previous literature, removing the memory cell degrades performance drastically.
In contrast, removing the S-RNN makes little to no difference in the final performance, suggesting that the memory cell alone is largely responsible for the success of LSTMs in NLP.
The results also confirm our hypothesis that weighted sums of context words is a powerful, yet more interpretable, model of contextual information.
We presented an alternate view of LSTMs: they are a hybrid of S-RNNs and a gated model that dynamically computes weighted sums of the S-RNN outputs.
Our experiments investigated whether the S-RNN is a necessary component of LSTMs.
In other words, are the gates alone as powerful of a model as an LSTM?
Results across four major NLP tasks (language modeling, question answering, dependency parsing, and machine translation) indicate that LSTMs suffer little to no performance loss when removing the S-RNN, but removing the gates can degrade performance substantially.
This provides evidence that the gating mechanism is doing the heavy lifting in modeling context, and that element-wise weighted sums of context-independent functions of the inputs are often as effective as fully-parameterized LSTMs.This work sheds light on the inner workings of the relatively opaque LSTM.
By removing the S-RNN and the output gate, we also show that the resulting model is a far more mathematically transparent variant of LSTMs.
This transparency enables a visualization of how the context affects the output of the model at every timestep, much like in attention-based models.
We hope that this new outlook on LSTMs will foster better and more efficient models of contextualization.
Gates do all the heavy lifting in LSTMs by computing element-wise weighted sums, and removing the internal simple RNN does not degrade model performance.