Is Attention All What You Need? -- An Empirical Investigation on Convolution-Based Active Memory and Self-Attention (1912.11959v2)

Abstract: The key to a Transformer model is the self-attention mechanism, which allows the model to analyze an entire sequence in a computationally efficient manner. Recent work has suggested the possibility that general attention mechanisms used by RNNs could be replaced by active-memory mechanisms. In this work, we evaluate whether various active-memory mechanisms could replace self-attention in a Transformer. Our experiments suggest that active-memory alone achieves comparable results to the self-attention mechanism for LLMling, but optimal results are mostly achieved by using both active-memory and self-attention mechanisms together. We also note that, for some specific algorithmic tasks, active-memory mechanisms alone outperform both self-attention and a combination of the two.

An Empirical Investigation of Self-Attention and Convolution-Based Active Memory in Transformer Models

The paper by Dowdell and Zhang provides an empirical investigation into the potential of convolution-based active-memory mechanisms to replace or complement self-attention in Transformer architectures. This research addresses the pivotal role of the self-attention mechanism in the widespread success of Transformer models for NLP tasks while exploring alternative methods for sequence modeling.

Core Investigation

The paper methodically compares self-attention mechanisms with various active-memory mechanisms inspired by the Neural GPU architecture. The specific active-memory mechanisms examined include traditional convolution operators, persistent-convolutional operators, and highway-convolutional operators. The experiments focus on two main tasks: LLMing and algorithmic challenge tasks, requiring different contextual and operational dependencies.

LLMing Experiments

In the LLMing task utilizing the WikiText-3 dataset, the paper reports that while self-attention outperforms active-memory mechanisms when used in isolation, integrating both mechanisms results in superior performance. Notably, highway-convolutional operators in conjunction with self-attention deliver the best results, underscoring the complementary nature of active-memory mechanisms to attention heads for LLMing tasks.

Algorithmic Task Experiments

The paper also evaluates both mechanisms across various algorithmic tasks, such as Reverse, Sort, Addition, and Multiply. In this context, active-memory mechanisms frequently outperform self-attention, particularly when tasks demand multiple token focus at each time-step. Remarkably, the research discovers that simple active-memory mechanisms can surpass self-attention, strengthening the hypothesis that active memory allows for versatile and efficient parallel sequence analysis.

Implications and Future Directions

The implications of these findings are significant for the design of deep learning models for sequence tasks, traditionally reliant on self-attention. The results suggest that fusing self-attention with convolution-based active memory could lead to more robust models capable of extracting broader context dependencies without compromising computational efficiency.

This paper opens several avenues for future research. One promising direction is exploring the broader applicability of this combined approach in sequence-to-sequence tasks like machine translation. Further analysis is also necessary to understand why certain algorithmic tasks benefit from active memory while others may suffer from integrating self-attention. The observed discrepancies could be pivotal in optimizing Transformer models for specific task categories.

Overall, this empirical paper enhances the understanding of different mechanisms to improve Transformer performance and suggests that a hybrid approach leveraging both self-attention and active-memory may achieve more optimal results for diverse sequential modeling tasks.