Depthwise Separable Convolutions for Neural Machine Translation (1706.03059v2)

Abstract: Depthwise separable convolutions reduce the number of parameters and computation used in convolutional operations while increasing representational efficiency. They have been shown to be successful in image classification models, both in obtaining better models than previously possible for a given parameter count (the Xception architecture) and considerably reducing the number of parameters required to perform at a given level (the MobileNets family of architectures). Recently, convolutional sequence-to-sequence networks have been applied to machine translation tasks with good results. In this work, we study how depthwise separable convolutions can be applied to neural machine translation. We introduce a new architecture inspired by Xception and ByteNet, called SliceNet, which enables a significant reduction of the parameter count and amount of computation needed to obtain results like ByteNet, and, with a similar parameter count, achieves new state-of-the-art results. In addition to showing that depthwise separable convolutions perform well for machine translation, we investigate the architectural changes that they enable: we observe that thanks to depthwise separability, we can increase the length of convolution windows, removing the need for filter dilation. We also introduce a new "super-separable" convolution operation that further reduces the number of parameters and computational cost for obtaining state-of-the-art results.

• The paper introduces SliceNet, a model that uses depthwise separable convolutions to significantly reduce parameter count and computational cost in NMT.
• It replaces filter dilation with larger convolution windows and integrates residual and super-separable convolutions for enhanced efficiency.
• Experimental results demonstrate that SliceNet attains state-of-the-art translation performance, challenging established NMT architectural approaches.

Depthwise Separable Convolutions for Neural Machine Translation: A Technical Analysis

The paper "Depthwise Separable Convolutions for Neural Machine Translation" explores the application of depthwise separable convolutions in the domain of Neural Machine Translation (NMT), presenting a novel architecture named SliceNet. This work capitalizes on the principles derived from successes in image classification with Xception and MobileNets, demonstrating its applicability in sequence-to-sequence tasks, specifically machine translation.

Depthwise separable convolutions, as introduced in the paper, are leveraged to significantly reduce the parameter count and computational cost associated with traditional convolutions while maintaining or even enhancing representational capacity. The authors demonstrate that their proposed SliceNet architecture achieves new state-of-the-art performance on machine translation tasks, notably with fewer resources.

Key Contributions

1. Introducing SliceNet: The architecture employs depthwise separable convolution layers with residual connections, inspired by the Xception network. SliceNet extends the efficiency of these layers to the sequence-to-sequence setting, effectively balancing parameter efficiency and computational demands.
2. Parameter Reduction Strategy: By using depthwise separable convolutions, SliceNet substantially reduces the parameter count relative to its predecessors, such as ByteNet, while achieving comparable or superior performance.
3. Architectural Innovations:
   • Removal of Filter Dilation: SliceNet avoids using filter dilation, a technique previously considered vital for expanding receptive fields in convolutional models for NMT. This is achieved by increasing convolution window sizes, enabled by the reduced cost of separable convolutions.
   • Introduction of Super-Separable Convolutions: A further generalization of separability that reduces parameter count more than standard separable convolutions by employing group factorizations within the depthwise convolution operation.

Experimental Insights

The empirical results provided in the paper underscore several significant findings:

• The use of depthwise separable convolutions in place of regular convolutions results in models that are more efficient and require fewer resources.
• Their experimentation shows no performance gains from filter dilation when larger convolution windows are feasible, challenging previous assumptions held by models like ByteNet and WaveNet.
• Super-separable convolutions offer incremental improvements over traditional depthwise separable convolutions, though at a slightly higher complexity.

Implications and Future Work

The adoption of separable convolution architectures in NMT opens pathways for further investigation into efficient architectures for other sequence-based tasks. This work also highlights the potential of separable convolutions in reducing the computational footprint of deep learning models without sacrificing performance.

While SliceNet represents an evolution in the design of machine translation models, future research could explore the scalability of super-separable convolutions to even larger models and their effectiveness in domains beyond NMT. Furthermore, the insights into avoiding filter dilation could have implications for other areas utilizing convolutional architectures, such as audio processing or video encoding.

In conclusion, SliceNet exemplifies the advancement of convolutional architectures in NLP, driving towards more efficient and powerful neural network designs. This paper contributes significantly to the ongoing effort to optimize deep learning models for practical and theoretical advancement.