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Title: R-TRANSFORMER: RECURRENT NEURAL NETWORK ENHANCED TRANSFORMER

Paper content:
Recurrent Neural Networks have long been the dominating choice for sequence modeling.
However, it severely suffers from two issues: impotent in capturing very long-term dependencies and unable to parallelize the sequential computation procedure.
Therefore, many non-recurrent sequence models that are built on convolution and attention operations have been proposed recently.
Notably, models with multi-head attention such as Transformer have demonstrated extreme effectiveness in capturing long-term dependencies in a variety of sequence modeling tasks.
Despite their success, however, these models lack necessary components to model local structures in sequences and heavily rely on position embeddings that have limited effects and require a considerable amount of design efforts.
In this paper, we propose the R-Transformer which enjoys the advantages of both RNNs and the multi-head attention mechanism while avoids their respective drawbacks.
The proposed model can effectively capture both local structures and global long-term dependencies in sequences without any use of position embeddings.
We evaluate R-Transformer through extensive experiments with data from a wide range of domains and the empirical results show that R-Transformer outperforms the state-of-the-art methods by a large margin in most of the tasks.
Recurrent Neural Networks (RNNs) especially its variants such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) have achieved great success in a wide range of sequence learning tasks including language modeling, speech recognition, recommendation, etc (Mikolov et al., 2010; Sundermeyer et al., 2012; Graves & Jaitly, 2014; Hinton et al., 2012; Hidasi et al., 2015) .
Despite their success, however, the recurrent structure is often troubled by two notorious issues.
First, it easily suffers from gradient vanishing and exploding problems, which largely limits their ability to learn very long-term dependencies (Pascanu et al., 2013) .
Second, the sequential nature of both forward and backward passes makes it extremely difficult, if not impossible, to parallelize the computation, which dramatically increases the time complexity in both training and testing procedure.
Therefore, many recently developed sequence learning models have completely jettisoned the recurrent structure and only rely on convolution operation or attention mechanism that are easy to parallelize and allow the information flow at an arbitrary length.
Two representative models that have drawn great attention are Temporal Convolution Networks(TCN) (Bai et al., 2018) and Transformer (Vaswani et al., 2017) .
In a variety of sequence learning tasks, they have demonstrated comparable or even better performance than that of RNNs (Gehring et al., 2017; Bai et al., 2018; Devlin et al., 2018) .
The remarkable performance achieved by such models largely comes from their ability to capture long-term dependencies in sequences.
In particular, the multi-head attention mechanism in Transformer allows every position to be directly connected to any other positions in a sequence.
Thus, the information can flow across positions without any intermediate loss.
Nevertheless, there are two issues that can harm the effectiveness of multi-head attention mechanism for sequence learning.
The first comes from the loss of sequential information of positions as it treats every position identically.
To mitigate this problem, Transformer introduces position embeddings, whose effects, The illustration of one layer of R-Transformer.
There are three different networks that are arranged hierarchically.
In particular, the lower-level is localRNNs that process positions in a local window sequentially (This figure shows an example of local window of size 3); The middle-level is multi-head attention networks which capture the global long-term dependencies; The upper-level is Position-wise feedforward networks that conduct non-linear feature transformation.
These three networks are connected by a residual and layer normalization operation.
The circles with dash line are the paddings of the input sequence however, have been shown to be limited (Dehghani et al., 2018; Al-Rfou et al., 2018) .
In addition, it requires considerable amount of efforts to design more effective position embeddings or different ways to incorporate them in the learning process (Dai et al., 2019) .
Second, while multi-head attention mechanism is able to learn the global dependencies, we argue that it ignores the local structures that are inherently important in sequences such as natural languages.
Even with the help of position embeddings, the signals at local positions can still be very weak as the number of other positions is significantly more.
To address the aforementioned limitations of the standard Transformer, in this paper, we propose a novel sequence learning model, termed as R-Transformer.
It is a multi-layer architecture built on RNNs and the standard Transformer, and enjoys the advantages of both worlds while naturally avoids their respective drawbacks.
More specifically, before computing global dependencies of positions with the multi-head attention mechanism, we firstly refine the representation of each position such that the sequential and local information within its neighborhood can be compressed in the representation.
To do this, we introduce a local recurrent neural network, referred to as LocalRNN, to process signals within a local window ending at a given position.
In addition, the LocalRNN operates on local windows of all the positions identically and independently and produces a latent representation for each of them.
In this way, the locality in the sequence is explicitly captured.
In addition, as the local window is sliding along the sequence one position by one position, the global sequential information is also incorporated.
More importantly, because the localRNN is only applied to local windows, the aforementioned two drawbacks of RNNs can be naturally mitigated.
We evaluate the effectiveness of R-Transformer with a various of sequence learning tasks from different domains and the empirical results demonstrate that R-Transformer achieves much stronger performance than both TCN and standard Transformer as well as other state-of-the-art sequence models.
The rest of the paper is organized as follows: Section 2 discusses the sequence modeling problem we aim to solve; The proposed R-Transformer model is presented in Section 3.
In Section 4, we describe the experimental details and discuss the results.
The related work is briefly reviewed in Section 5.
Section 6 concludes this work.
In summary, experimental results have shown that the standard Transformer can achieve better results than RNNs when sequences exhibit very long-term dependencies, i.e., sequential MNIST while its performance can drop dramatically when strong locality exists in sequences, i.e., polyphonic music and language.
Meanwhile, TCN is a very strong sequence model that can effectively learn both local structures and long-term dependencies and has very stable performance in different tasks.
More importantly, the proposed R-Transformer that combines a lower level LocalRNN and a higher level multi-head attention, outperforms both TCN and Transformer by a large margin consistently in most of the tasks.
The experiments are conducted on various sequential learning tasks with datasets from different domains.
Moreover, all experimental settings are fair to all baselines.
Thus, the observations from the experiments are reliable with the current experimental settings.
However, due to the computational limitations, we are currently restricted our evaluation settings to moderate model and dataset sizes.
Thus, more evaluations on big models and large datasets can make the results more convincing.
We would like to leave this as one future work.
In this paper, we propose a novel generic sequence model that enjoys the advantages of both RNN and the multi-head attention while mitigating their disadvantages.
Specifically, it consists of a LocalRNN that learns the local structures without suffering from any of the weaknesses of RNN and a multi-head attention pooling that effectively captures long-term dependencies without any help of position embeddings.
In addition, the model can be easily implemented with full parallelization over the positions in a sequence.
The empirical results on sequence modeling tasks from a wide range of domains have demonstrated the remarkable advantages of R-Transformer over state-of-the-art nonrecurrent sequence models such as TCN and standard Transformer as well as canonical recurrent architectures.

assistant: text: This paper proposes an effective generic sequence model which leverages the strengths of both RNNs and Multi-head attention.