论文标题
两者中最好的：一种与声明性事实的多跳解释的混合方法
Best of Both Worlds: A Hybrid Approach for Multi-Hop Explanation with Declarative Facts
论文作者
论文摘要
支持语言的AI系统可以回答复杂的，多跳的问题以高精度，但是用证据支持答案是一项更具挑战性的任务，这对于对用户的透明度和可信度很重要。该领域的先前工作通常可以在效率和准确性之间进行权衡；最先进的深度神经网络系统太麻烦了，无法在大规模应用中有用，而最快的系统缺乏可靠性。在这项工作中，我们将快速的句法方法与强大的语义方法集成了基于声明性事实的多跳解释生成。我们的最佳系统学习了一个轻巧的操作，以模拟对证据和微型语言模型的多跳上推理，以重新升级生成的解释链，优于先前的工作中纯粹的句法基线，最多可以在黄金解释检索率中使用7％。
Language-enabled AI systems can answer complex, multi-hop questions to high accuracy, but supporting answers with evidence is a more challenging task which is important for the transparency and trustworthiness to users. Prior work in this area typically makes a trade-off between efficiency and accuracy; state-of-the-art deep neural network systems are too cumbersome to be useful in large-scale applications, while the fastest systems lack reliability. In this work, we integrate fast syntactic methods with powerful semantic methods for multi-hop explanation generation based on declarative facts. Our best system, which learns a lightweight operation to simulate multi-hop reasoning over pieces of evidence and fine-tunes language models to re-rank generated explanation chains, outperforms a purely syntactic baseline from prior work by up to 7% in gold explanation retrieval rate.
