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该项目开源了一种可解释的、高效的多文档阅读理解算法SAE(Select, Answer and Explain)。该算法通过选择、回答和解释来对多个信息源进行推理,并通过提供支持证据来解释答案预测。具体地,SAE算法首先过滤掉与答案无关的文件, 从而减少干扰信息的数量。然后将选定与答案相关的文档输入到一个模型中, 预测答案和提供支持的句子。 该模型在答案预测的单词级别和支持句子预测的句子级别同时优化了多任务学习目标, 并通过注意力机制实现了这两个任务的交互。

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