Attending via both Fine-tuning and Compressing

Though being a primary trend for enhancing interpretability of neural networks, attention mechanism's reliability and validity are still under debate. In this paper, we try to purify attention scores to obtain a more faithful explanation of downstream models. Specifically, we propose a framework consisting of a learner and a compressor, which performs fine-tuning and compressing iteratively to enhance the performance and interpretability of the attention mechanism. The learner focuses on learning better text representations to achieve good decisions by fine-tuning, while the compressor aims to perform compressions over the representations to retain the most useful clues for explanations with a Variational information bottleneck ATtention (VAT) mechanism. Extensive experiments on eight benchmark datasets show the great advantages of our proposed approach in terms of both performance and interpretability.