论文标题

每个决定的注意力矩阵:基于忠诚的仲裁在文本分类中对变形金刚的多种基于注意力的解释

An Attention Matrix for Every Decision: Faithfulness-based Arbitration Among Multiple Attention-Based Interpretations of Transformers in Text Classification

论文作者

Mylonas, Nikolaos, Mollas, Ioannis, Tsoumakas, Grigorios

论文摘要

变压器广泛用于自然语言处理中,它们始终如一地实现最先进的性能。这主要是由于他们基于注意力的架构,这使他们能够在(子)单词之间建模丰富的语言关系。但是,变压器很难解释。能够为其决策提供推理是人类生命受到影响的领域模型的重要特性。随着变压器在此类领域中发现广泛使用,因此需要为其量身定制的可解释性技术。我们提出了一种新技术,该技术在几种可以通过组合不同的头,层和矩阵操作来获得的几种基于注意力的解释中选择了最忠实的解释。此外,引入了两种变体,用于(i)降低计算复杂性,从而更快,对环境更友好,以及(ii)增强多标签数据中的性能。我们进一步提出了一个新的忠诚度量标准,该指标更适合变压器模型,并与基于地面真理理由的Precision-Recall曲线下的区域表现出很高的相关性。我们通过在七个数据集上进行一系列定量和定性实验来验证我们的贡献实用性。

Transformers are widely used in natural language processing, where they consistently achieve state-of-the-art performance. This is mainly due to their attention-based architecture, which allows them to model rich linguistic relations between (sub)words. However, transformers are difficult to interpret. Being able to provide reasoning for its decisions is an important property for a model in domains where human lives are affected. With transformers finding wide use in such fields, the need for interpretability techniques tailored to them arises. We propose a new technique that selects the most faithful attention-based interpretation among the several ones that can be obtained by combining different head, layer and matrix operations. In addition, two variations are introduced towards (i) reducing the computational complexity, thus being faster and friendlier to the environment, and (ii) enhancing the performance in multi-label data. We further propose a new faithfulness metric that is more suitable for transformer models and exhibits high correlation with the area under the precision-recall curve based on ground truth rationales. We validate the utility of our contributions with a series of quantitative and qualitative experiments on seven datasets.

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