A popular end-to-end architecture for selective rationalization is the select-then-predict pipeline, comprising a generator to extract highlights fed to a predictor. Such a cooperative system suffers from suboptimal equilibrium minima due to the dominance of one of the two modules, a phenomenon known as interlocking. While several contributions aimed at addressing interlocking, they only mitigate its effect, often by introducing feature-based heuristics, sampling, and ad-hoc regularizations. We present GenSPP, the first interlocking-free architecture for selective rationalization that does not require any learning overhead, as the above-mentioned. GenSPP avoids interlocking by performing disjoint training of the generator and predictor via genetic global search. Experiments on a synthetic and a real-world benchmark show that our model outperforms several state-of-the-art competitors.

翻译：选择性合理化的一种流行端到端架构是"先选择后预测"流水线，包含一个生成器用于提取特征亮点并输入预测器。这种协作系统由于两个模块中某一方的支配作用而陷入次优均衡极小值，这种现象被称为互锁。尽管已有若干研究致力于解决互锁问题，但它们仅通过引入基于特征的启发式方法、采样技术和临时正则化等手段缓解其影响。我们提出了GenSPP，这是首个无需任何学习开销的无互锁选择性合理化架构。GenSPP通过遗传全局搜索对生成器和预测器进行解耦训练，从而避免互锁现象。在合成数据集和真实世界基准测试上的实验表明，我们的模型性能优于多种先进竞争方法。