Modeling Localness for Self-Attention Networks (1810.10182v1)

Abstract: Self-attention networks have proven to be of profound value for its strength of capturing global dependencies. In this work, we propose to model localness for self-attention networks, which enhances the ability of capturing useful local context. We cast localness modeling as a learnable Gaussian bias, which indicates the central and scope of the local region to be paid more attention. The bias is then incorporated into the original attention distribution to form a revised distribution. To maintain the strength of capturing long distance dependencies and enhance the ability of capturing short-range dependencies, we only apply localness modeling to lower layers of self-attention networks. Quantitative and qualitative analyses on Chinese-English and English-German translation tasks demonstrate the effectiveness and universality of the proposed approach.

• The paper introduces a learnable Gaussian bias to re-shape attention distributions and emphasize local context.
• It applies localness modeling in lower network layers to effectively capture short-range dependencies.
• Experimental results on Chinese-English and English-German tasks demonstrate superior performance over standard Transformers.

Modeling Localness for Self-Attention Networks

The paper "Modeling Localness for Self-Attention Networks" explores the limitations of conventional self-attention networks in capturing local dependencies, which are often critical for various natural language processing tasks. The authors propose a novel approach to enhance self-attention models by incorporating localness through a learnable Gaussian bias. This bias serves to concentrate attention on nearby elements, thus bolstering the model's capacity to capture short-range dependencies while preserving its ability to handle long-range dependencies inherent in self-attention mechanisms.

The traditional self-attention mechanism, as exemplified by the Transformer architecture, excels in capturing global dependencies by attending to all elements in a sequence simultaneously. However, this can lead to a dilution of local context, as attention is spread across a wide set of possibilities. The proposed solution introduces a learnable Gaussian bias to the attention distribution, effectively re-shaping it to prioritize local context. By manipulating both the center and the scope of the local region, defined via a central position and a dynamic window, attention is concentrated around relevant local elements.

The implementation of localness is targeted at the lower layers of self-attention networks, as these layers are more effective in modeling short-range dependencies. This allows higher layers to maintain their focus on long-range dependencies, aligning with recent findings in hierarchical model configurations. Such a design choice is supported by experimental results on machine translation tasks between Chinese and English, and English to German, which demonstrate improvements in translation performance over standard Transformer models.

The results indicate substantive benefits of integrating localness modeling within the self-attention framework. In conjunction with relative position encoding, another locality modeling approach, further gains in model performance are observed. This synergy suggests that distinct strategies for enhancing locality can be complementary, ultimately contributing to more accurate machine translation systems.

As for theoretical implications, this research underscores the importance of balancing local and global dependencies in neural architectures. The findings imply that the introduction of learnable biases within attention distributions could be broadly applicable across different models and tasks, potentially improving performance where local context is vital.

Future research could explore additional mechanisms for incorporating linguistic insights, such as syntactic features or phrase structures, into localness modeling. It could also examine the application of these principles to other domains, such as speech recognition or sentiment analysis, where the interplay between local and global context is crucial.

In conclusion, the work on modeling localness for self-attention networks offers a compelling advancement in refining attention mechanisms, enhancing their capability to accurately capture and utilize contextual dependencies in sequence-based tasks.