轻量级问答系统在复杂问题上表现更优且响应更快
LiCQA : A Lightweight Complex Question Answering System
- 基于语料证据的无监督方法，无需知识图谱或大量训练数据
- 在基准测试中显著超越两个顶尖系统，延迟大幅降低
- 适合资源受限场景下的高效复杂问答应用
过去二十年间，问答系统设计取得显著进展。然而，针对答案分散于多文档的复杂问题，仍具挑战性。现有系统或依赖知识图谱，或使用需大量计算资源和训练数据的神经模型。本文提出LiCQA，一种基于语料证据的无监督问答模型。我们实证比较了LiCQA与两种基于不同原理的最新系统的有效性与效率。实验结果表明，LiCQA在基准数据集上显著优于这两个先进系统，同时实现显著更低的延迟。
原文摘要 · Abstract (English)
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.
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