An Analysis of Pervasive Attention in 2D Convolutional Networks for Sequence-to-Sequence Prediction
In this paper, the authors propose a novel architecture for sequence-to-sequence prediction tasks, specifically focusing on machine translation. The current state-of-the-art methods predominantly leverage encoder-decoder architectures augmented with attention mechanisms. These traditional setups encode the input sequence and generate the output sequence in discrete steps, interfacing by recombining encoded source tokens with the decoder state through attention mechanisms.
The core contribution of this research is the introduction of a Pervasive Attention model that departs from this traditional framework. Instead, it employs a single 2D convolutional neural network (CNN) designed to simultaneously process both input and output sequences. Each layer in this network iteratively integrates information from the target sequence generated thus far, into the source token encoding process. This integration endows the network with intrinsic attention-like qualities, allowing it to model sequence dependencies more effectively and with fewer parameters compared to conventional encoder-decoder architectures.
Model Architecture
The proposed model utilizes DenseNet, a highly connected type of CNN, well-known for its efficacy in image classification tasks. Layers are densely connected, allowing them to take inputs from all preceding layers, which facilitates gradient propagation. Masked convolutional filters ensure that information from future target tokens is never accessed, adhering to the autoregressive nature of sequence generation.
The model forms a 2D grid from parallel input and output sequences, upon which the CNN applies its layers. Attention is inherently integrated across every layer, contrasting with typical architectures where attention is a discrete module.
Experimental Evaluation
The model's performance was evaluated on the IWSLT 2014 German-English translation tasks. Notable is its comparison with competitive baselines like RNNsearch, ConvS2S, and Transformer. The results highlight the model's efficacy; it matched or surpassed the BLEU scores of these competing architectures while maintaining a simpler design and fewer parameters.
The authors explore variations of the model, such as different pooling mechanisms (max-pooling, average-pooling, and self-attention) to aggregate information, providing insights into their impact on performance. The implicit sentence alignments produced by max-pooling and self-attention illustrate that the proposed model effectively captures alignment without explicit attention mechanisms.
Implications and Speculative Future Directions
This research marks a shift towards integrating source and target sequence processing, bypassing the traditional encoder-decoder separation. The reduced complexity in terms of parameters could lead to more efficient and scalable models, potentially favorable for deployment scenarios with resource constraints.
Further exploration into hybrid models that complement this 2D CNN approach with preliminary 1D embeddings through LSTM or simpler CNNs could lead to further performance gains. Additionally, expanding this framework to accommodate multilingual pairings could broaden the model's applicability in global translation systems.
In conclusion, the Pervasive Attention model offers a promising alternative to conventional sequence-to-sequence architectures. Its performance parity with more complex models and capacity for intrinsic attention integration advocates for broader research into unified CNN approaches to sequence processing tasks.