Over the last twenty years, significant progress has been made in designing and implementing Question Answering (QA) systems. However, addressing complex questions, the answers to which are spread across multiple documents, remains a challenging problem. Recent QA systems that are designed to handle complex questions work either on the basis of knowledge graphs, or utilise contem- porary neural models that are expensive to train, in terms of both computational resources and the volume of training data required. In this paper, we present LiCQA, an unsupervised question answer- ing model that works primarily on the basis of corpus evidence. We empirically compare the effectiveness and efficiency of LiCQA with two recently presented QA systems, which are based on different underlying principles. The results of our experiments show that LiCQA significantly outperforms these two state-of-the-art systems on benchmark data with noteworthy reduction in latency.

翻译：过去二十年中，问答系统的设计与实现取得了显著进展。然而，处理答案分散在多个文档中的复杂问题仍然是一个具有挑战性的难题。当前用于处理复杂问题的问答系统要么基于知识图谱构建，要么采用需要大量计算资源和训练数据的当代神经模型，其训练成本高昂。本文提出LiCQA——一种主要基于语料证据的无监督问答模型。我们通过实证方法将LiCQA与近期提出的两种基于不同原理的问答系统在效能与效率方面进行比较。实验结果表明，在基准数据集上，LiCQA在显著降低延迟的同时，性能明显优于这两种前沿系统。