Neural Machine Translation in Linear Time (1610.10099v2)

Abstract: We present a novel neural network for processing sequences. The ByteNet is a one-dimensional convolutional neural network that is composed of two parts, one to encode the source sequence and the other to decode the target sequence. The two network parts are connected by stacking the decoder on top of the encoder and preserving the temporal resolution of the sequences. To address the differing lengths of the source and the target, we introduce an efficient mechanism by which the decoder is dynamically unfolded over the representation of the encoder. The ByteNet uses dilation in the convolutional layers to increase its receptive field. The resulting network has two core properties: it runs in time that is linear in the length of the sequences and it sidesteps the need for excessive memorization. The ByteNet decoder attains state-of-the-art performance on character-level LLMling and outperforms the previous best results obtained with recurrent networks. The ByteNet also achieves state-of-the-art performance on character-to-character machine translation on the English-to-German WMT translation task, surpassing comparable neural translation models that are based on recurrent networks with attentional pooling and run in quadratic time. We find that the latent alignment structure contained in the representations reflects the expected alignment between the tokens.

• The paper introduces ByteNet, a novel CNN-based architecture that processes sequences in linear time for efficient neural machine translation.
• It employs a dynamic unfolding mechanism with dilated convolutions to handle variable source and target sequence lengths effectively.
• Experimental results show superior BLEU scores on English-to-German tasks, outperforming traditional RNN-based translation systems.

Neural Machine Translation in Linear Time

This paper introduces a novel approach to neural machine translation using the ByteNet framework, which leverages one-dimensional convolutional neural networks (CNNs) to achieve efficient sequence processing. The architecture presents a unique solution to the computational challenges inherent in traditional recurrent neural networks (RNNs) by ensuring linear running time relative to sequence length. The ByteNet is designed with two core components: an encoder to process source sequences and a decoder to generate target sequences, both interconnected to preserve temporal resolution.

Architectural Innovations

A key innovation in ByteNet lies in its method for handling differing sequence lengths between source and target languages. This is achieved through a dynamic unfolding mechanism, which circumvents the constraint of constant-size representations. By unfolding the decoder dynamically over the encoder's representation, ByteNet maintains the ability to effectively process sequences of varied lengths. The use of dilation in the convolutional layers is another notable feature, allowing the model to expand its receptive field rapidly without increasing computational complexity.

The architecture's computational efficiency is underlined by its linear time complexity, enabling parallel processing of sequences during both training and evaluation. This is in contrast with RNNs, which struggle with long-range dependencies due to their inherent serial nature.

Performance and Results

The ByteNet demonstrates state-of-the-art performance across several benchmarks. It surpasses existing character-level models in LLMing tasks, achieving a notable 1.31 bits/character on the Hutter Prize Wikipedia dataset. In the domain of machine translation, ByteNet excels in the English-to-German WMT tasks, significantly outperforming traditional RNN-based systems. The model achieves BLEU scores of 22.85 and 25.53 on the 2014 and 2015 test sets, respectively, demonstrating its effectiveness over recurrent architectures.

Implications and Future Work

The research contributes significantly to both theoretical and practical aspects of machine translation. Theoretically, the model's design highlights a pathway to scalable and efficient sequence processing by dissociating translation tasks from memorization, thus allowing better learning of long-range dependencies. Practically, the linear running time and parallel capability point towards more efficient deployments of neural translation models in real-world applications.

Future directions include exploring enhancements in network depth and dilation strategies to further improve learning capabilities. Additional work could also investigate the extension of ByteNet to other languages and domains, testing its robustness and adaptability. Moreover, the integration of ByteNet with other linguistic tasks could open new frontiers in natural language processing.

In summary, the ByteNet represents a significant advancement in neural machine translation, offering a blend of efficiency and performance that addresses the limitations of previous models. Its architecture serves as a compelling template for future research in sequence modeling.