Convolutional Sequence to Sequence Learning

Sequence-to-sequence learning has traditionally relied on recurrent neural networks like LSTM and GRU, which, despite their effectiveness, suffer from slow training and limited parallelism. The advent of Transformer models introduced attention mechanisms that improved long-range dependency modeling but at high computational costs. This paper proposes a novel approach: a fully convolutional sequence-to-sequence model that leverages deep stacks of 1D convolutions combined with gated linear units (GLU) and residual connections. By replacing RNNs with convolutional layers, the model achieves remarkable training speedups—up to ten times faster—while surpassing state-of-the-art BLEU scores on multiple large-scale translation benchmarks, including WMT'14 English-German and English-French. The architecture incorporates position encoding to maintain sequence order and multi-step attention modules at each decoder layer, enabling hierarchical focus on input features. Extensive experiments demonstrate that this model not only accelerates training but also improves translation quality, setting new standards in NLP. Its parallelizable design makes it highly scalable, suitable for industrial deployment, and adaptable to other sequence tasks like summarization. The work signifies a paradigm shift, showing that convolutional architectures, when combined with attention, can rival and even outperform traditional RNN-based models in both efficiency and accuracy. Future research will explore sparser convolutional structures, pretraining, and multimodal extensions, promising broader impacts across AI applications.